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CEO Experience and Financial Reporting Quality:

Evidence from Management Forecasts

Paul Brockman

Perella Department of Finance

Lehigh University

621 Taylor Street, Bethlehem, PA 18015

e-mail: [email protected]

John L. Campbell*

J.M. Tull School of Accounting

University of Georgia


Hye Seung (Grace) Lee

Department of Accounting and Taxation

Gabelli School of Business, Fordham University

45 Columbus Avenue, New York, NY 10023


Jesus M. Salas

Perella Department of Finance

Lehigh University

621 Taylor Street, Bethlehem, PA 18015


May 2018

* Corresponding Author. We appreciate helpful comments and suggestions from Steve Baginski, Ted

Christensen, James Chyz, Owen Davidson, Dan Dhaliwal, Fabio Gaertner, Reynolde Pereira, Santhosh

Ramalingegowda, Logan Steele, Jake Thornock, Ben Whipple, and workshop participants at Lehigh

University, the University of Georgia, the Financial Management Association’s (FMA) Annual Conference

and the American Accounting Association’s (AAA) Annual Meeting.

mailto:[email protected]




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CEO Experience and Financial Reporting Quality:

Evidence from Management Forecasts

ABSTRACT

Internally-promoted CEOs should have a deeper understanding of their firm’s products, supply

chain, operations, business climate, corporate culture, and how to navigate among employees to

get the information they need. Thus, we argue that internally-promoted CEOs are likely to produce

higher quality financial reports than outsider CEOs. Using a sample of U.S. firms from the

Standard & Poor’s (S&P) 1,500 index from 1995 to 2011, we hand-collect whether a CEO is hired

from inside the firm and, if so, the number of years they worked at the firm before becoming CEO.

We then examine whether managers with more internal experience issue higher quality financial

information and offer three main findings. First, CEOs with more internal experience are more

likely to issue voluntary earnings forecasts than those managers with less internal experience as

well as those managers hired from outside the firm. Second, CEOs with more internal experience

issue more accurate earnings forecasts than those managers with less internal experience as well

as those managers hired from outside the firm. Finally, investors react more strongly to forecasts

issued by insider CEOs than to those issued by outsider CEOs. Overall, our findings suggest that

when managers have work experience with the firm prior to taking the CEO position, the firm’s

financial reporting is of higher quality.

Key words: Voluntary disclosure; CEO internal experience; Investor reaction

JEL Descriptors: M40, M41, M49, G14

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1. Introduction

A long literature debates the value of experience inside the firm before becoming CEO.

This research mostly examines the effect of a new CEO on firm performance after the manager

takes office, and offers mixed findings. In certain settings, a firm’s future performance is better if

they bring in a CEO from outside who can take a “fresh look.” The argument is that these managers

are not constrained by doing things the same way as their predecessors, so they may engage in

value-increasing risk-taking after being hired. In other settings, however, a firm’s future

performance is better if they promote a CEO that is an insider who best understands how things

operate. The argument is that these managers do not need to spend time learning how the firm or

its accounting system works. While prior research has used these arguments to examine the effect

of turnover on firm performance after a CEO takes office, it is silent as to the effects of hiring an

insider or outsider on the firm’s financial reporting quality.

In this study, we examine the effect of CEO internal experience on financial reporting

quality, using management forecast characteristics as a proxy for the quality of a firm’s financial

reporting. We expect management forecasts to be a powerful setting because they are (1) forward

looking and thus more in line with the activities of a CEO (i.e., more likely to rely on firm-specific

expertise outside of accounting/finance), and (2) not audited or otherwise formally reviewed by an

outside party. Specifically, we examine three research questions. First, do CEOs promoted from

within the firm (CEOs with prior internal experience) issue more frequent and more accurate

management forecasts? Second, do the number of years of prior internal experience have an effect

on the frequency and accuracy of management forecasts? Finally, if so, do investors respond to

management forecasts as if they understand that these CEOs provide better information?

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The extent to which a CEO’s prior internal experience affects financial reporting quality is

of interest to both academics and practitioners. Relying on upper echelons theory (Hambrick and

Mason 1984), prior research examines whether managers’ operating and reporting decisions are

influenced by personal characteristics such as age, education, financial and legal expertise, and

personal risk aversion (Bamber, Jiang, and Wang 2010; Dyreng, Hanlon, and Maydew 2010; Chyz

2013; Call, Campbell, Dhaliwal, and Moon 2017). We argue that the location of a CEO’s prior

work experience also has an effect on reporting outcomes. Furthermore, a firm’s shareholders and

board of directors are responsible for hiring a CEO. Oftentimes, a critical question is whether to

promote an internal employee or hire an outsider for the CEO position. We examine whether the

firm’s future financial reporting quality is associated with this decision.

Internal CEOs have two distinct advantages over outside hires. First, internal CEOs have a

deep knowledge of their firms. They already understand the firm’s products, supply chain,

operations, business climate, corporate culture, and how to navigate among employees to get the

information they need. Second, internal CEOs are significantly less expensive than outside hires

because outsiders require additional pay to compensate them for taking the risk of moving to a

new firm (Reda and Wert 2013; Cadman, Campbell, and Klasa 2016). Because of these benefits,

it is not surprising that the majority of CEOs (i.e., approximately 80 percent) come from within

the firm (Khurana 2002). On the other hand, prior research shows that outside CEOs have a more

diverse skill set that could be especially valuable in managing firms in today’s business

environment and that, as a result, the percentage of CEOs hired from outside the firm is increasing

(Murphy and Zabojnik 2004; Kaplan and Minton 2012). Thus it is possible that outside CEOs have

a better understanding of macroeconomic factors and investor needs that could lead to higher

quality reporting.

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We follow prior literature on CEO succession and measure outside CEOs using a dummy

variable equal to one if an executive was not employed by the firm prior to becoming CEO, and

zero otherwise. However, we view this as an incomplete measure because it considers a CEO who

came to the firm one year before promotion as much of an insider as an executive who spent twenty

years in the firm prior to promotion. Consequently, we hand-collect data on the number of years

the CEO worked for the firm prior to promotion, and use this as a second, continuous measure of

“insiderness.”1 This alternative measure should better capture the degree to which a manager

knows the firm. Throughout our tests, we use both the discrete and continuous measures for the

extent to which a newly appointed CEO is an insider.

Using a sample consisting of U.S. firms from the Standard & Poor’s (S&P) 1,500 index

between 1995 and 2011, we provide three main findings. First, we find that CEOs hired from inside

the firm are more likely to issue a management forecast. Second, we find that CEOs hired from

inside the firm issue management forecasts that are more accurate. Both of these results hold using

a continuous measure that represents the number of years the manager worked for the firm prior

to taking office, and suggest that firms’ financial reporting quality improves when a manager has

greater inside knowledge of the firm prior to taking office. Finally, we find that investors place a

greater weight on the news conveyed by management forecasts when they are issued by CEOs

hired from inside the firm. However, this result does not extend to the number of years the CEO

worked for the firm prior to taking office. Overall, our findings suggest that when managers have

work experience with the firm prior to taking office, the firm’s financial reporting quality improves.

As with all empirical work and particularly in CEO turnover and succession research, our

tests represent associations for which we cannot definitively ascribe causality. We attempt to rule

1 This measure is not broadly available in commercial, machine-readable datasets. Thus, as explained more fully in the Research

Design section, we hand-collect this variable by reading CEO biographies in annual proxy filings and on websites like Forbes.

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out the possibility that our results are driven by a correlated omitted variable that simultaneously

leads to externally hired CEOs and poor financial reporting quality by controlling for several

factors (i.e., firm governance, pre-turnover performance, firm riskiness, and CEO characteristics

other than their prior work experience), as well as the application of both propensity score

matching and two-stage least squares. Furthermore, we note that any alternative explanation to our

results would require an association with financial reporting quality in the exact same manner as

CEO internal experience. Despite our inability to identify any plausible such correlated omitted

variable, it is possible that one exists and we have not adequately controlled for it. Nevertheless,

because our tests focus on the time periods after the new CEO is hired, our findings imply that

when a new CEO has internal experience, that firm’s financial reporting quality improves,

regardless of the reason the new CEO is hired. Finally, a limitation of our study is that our sample

only includes U.S. firms that are subject to the specific legal, enforcement, and business

environment of the U.S. Future work may wish to examine whether our findings generalize to

other countries.

Our study makes several contributions to the academic literature. First, we contribute to

the literature on CEO turnover and succession. Prior research shows that pre-turnover performance

is a main determinant of succession origin (Finkelstein, Hambrick and Canella, 2009). Most firms

prefer to hire an insider to an outsider for the CEO position, but there has been a shift to hire more

outsider CEOs in the last 20 years and the literature is now debating possible explanations for this

change. For example, Murphy and Zabojnik (2004) propose that firm specialists are not as

important today. In this paper, we show that insiders’ firm specific knowledge leads to more

accurate management forecasts. We also contribute to the literature that studies the impact of CEO

characteristics on management forecasts. For example, Baik, Farber and Lee (2011) find that more

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able CEOs issue more and more accurate forecasts than less able CEOs. Our study is different

because our CEO characteristic is not transferrable. CEO internal experience is not really ability

per se in that a CEO’s internal experience in one firm is not as useful in another firm. CEO internal

experience drops to zero when an individual changes firms. Thus, outsiders who potentially have

high ability always have zero internal experience in our sample. We study whether this internal

experience is valuable to investors with regards to managerial forecasts.

Our study also contributes to the literature on managerial fixed effects such as that of

Bertrand and Schoar (2003) and Dejong and Ling (2013) in corporate policies, Dyreng, Hanlon,

and Maydew (2010) in tax aggressiveness and Bamber, Jiang, and Wang (2010) in voluntary

disclosures. These studies find that many different corporate decisions vary significantly across

managers. In other words, these studies find that manager characteristics (as compared to firm

characteristics) play a significant role in corporate policies. In this study, we test whether a specific

CEO characteristic, (internal experience) affects incidence and quality of managerial forecasts.

Finally, this study contributes to the recent literature that identifies specific CEO

characteristics (such as CEO ability and overconfidence) that affect managerial forecasts. In a

recent literature review of voluntary disclosures, Hirst, Koonce, and Venkataraman (2008)

conclude that “...managers’ choice of forecast characteristics appears to be the least understood

(both in terms of theory and research) even though it is the component over which managers have

the most control.” We help address this hole in the literature by pointing to a managerial

characteristic that is directly related to the information environment in firms. We argue that CEOs

with high internal experience produce more and better forecasts partly because they know more

about the firm than CEOs with low internal experience.

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2. Background and hypothesis development

Determinants of succession origin

To better understand the differences between inside and outside CEO replacements, we

need to examine how the firm chooses a CEO. Levinson (1974) and Cannella and Lubatkin (1993)

point out that incumbent CEOs often try to pick a successor who will extend their own strategies.

Alternatively, boards of directors try to pick successors that are similar to the board. For example,

prior research find that boards dominated with outside directors are more likely to replace an

incumbent CEO with an outsider, especially when past performance is poor (Zajac and Westphal

1996; Westphal and Fredrickson 2001; Dahya and McConnell 2005). Finally, Shen and Cannella

(2002) propose that other top management team members can use their power to fire and take over

for an incumbent CEO.

Today, most firms replace a departing CEO with an insider (in our sample, about 70% of

departing CEOs are replaced with insiders). 2 Thus, deviating from the norm by replacing a

departing CEO with an outsider is a strong indicator that the board wants to signal a significant

change in leadership (Vancil, 1987). It makes sense then that the strongest determinant of

succession origin that has been identified by researchers is past performance. Specifically, prior

research finds that firms usually replace an incumbent CEO with an outsider when the firm

performed poorly in the past (Boeker and Goodstein 1993; Cannella and Lubatkin 1993; Kang and

Shivdasani 1995; Huson, Parrino and Starks 2001; Shen and Cannella 2002).

Antecedents and attributes of voluntary management forecasts

Managerial forecasts are voluntary disclosures that managers make before earnings

releases in order to aid investors and analysts as they make investment recommendations and

2 Pessarossi and Weill (2013) find this same phenomenon in China, as in 58 percent of the time, successor CEOs in their sample

are hired from internally within the firm.

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decisions. Regulation changes in disclosure were intended to promote the use of voluntary

management forecasts.3 While it is possible that managers could have used these safe harbor

protections to strategically disclose incorrect information, research has found that these regulatory

changes led to improvements in forecast accuracy (Johnson et al. 2001). Thus, literature on

voluntary disclosures generally finds that forecast figures are not manipulated for some ulterior

motives, on average. The timing of forecasts, however, has been found to be opportunistic for

managers. For example, Brockman, Khurana, and Martin (2008), and Aboody and Kasznik (2000)

identify cases in which managers time the issuance of voluntary disclosures for opportunistic

reasons.

The next relevant question is therefore, who is most likely to issue voluntary forecasts?

The literature has identified several firm level characteristics that drive forecast incidence. For

example, the likelihood of litigation, anticipated firm performance, firm size, growth opportunities,

earnings volatility, etc. are all potential determinants of forecast incidence.4 More relevant to our

study, prior research argues that manager-specific characteristics affect the likelihood that a firm

issues a forecast. For example, Hilary and Hsu (2011) show that recent success in forecast accuracy

causes managers to become overconfident. This overconfidence leads managers to deviate more

from public signals of firm performance. In addition, this overconfidence is associated with poor

subsequent forecast accuracy. Furthermore, Hribar and Yang (2016) show that overconfidence

(measured by media citations and early executive option exercises) is associated with higher

incidence of forecasts. CEO ability has also been shown to affect the likelihood of issuing

voluntary forecasts. Specifically, Baik, Farber and Lee (2011) rely on theory by Trueman (1986)

3 Most recently, the 1996 Private Securities Litigation Reform Act (PSLRA) extends previous safe harbor procedures so that firms

could not be easily sued for providing voluntary forecasts, even if these forecasts do not materialize. 4 Examples of studies that propose firm determinants of forecasts include Skinner (1994, 1997), Rogers and Stocken (2005), Field

et al. (2005), Coller and Yohn (1997) and Gong, Li and Zhou (2013) among others.

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to argue that high-ability CEOs wish to signal their high ability of anticipating changes in firm

prospects by issuing forecasts.

This extant literature on manager characteristics and management forecasts argues that

managers who believe they can issue accurate forecasts are more likely to issue forecasts than

those who do not believe they can provide accurate forecasts. Part of the reason for this is that

investors value the information provided by forecasts, and there can be significant career concerns

and reputational costs associated with providing inaccurate information. For example, Lee,

Matsunaga and Park (2012) find that forecast accuracy is inversely related to the likelihood of

CEO turnover. That is, they find that boards of directors use forecast accuracy as an indicator of

CEO ability. This finding implies that CEOs who fear they are not able to provide accurate

forecasts may shy away from issuing forecasts altogether. More recently, Baginski, Campbell,

Hinson, and Koo (2018) provide evidence that when firms contract with their managers to reduce

their career concerns, they provide more accurate and truthful management forecasts. This finding

suggests that managers are less likely to provide forecasts if their career concerns are high.

More specific to our variable of interest, Trueman (1986) proposes that managers issue

forecasts in order to show their ability to anticipate potential changes in economic conditions and

adjust production levels given anticipated changes in expected demand. This is because investors

can later verify this ability when actual earnings are reported. In addition, prior research argues

that insider CEOs have more in depth knowledge of the firm’s products, employees, suppliers, etc.

than outsider CEOs (Kotter 1982). Deep knowledge of the firm can in turn help managers

anticipate changes in prospects. For example, managers who know competitors and suppliers

better are also more likely to identify problems with those competitors and suppliers. In addition,

managers who spend more time in the firm before becoming CEOs have better relationships with

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key subordinate managers and therefore could get better information and more honest feedback

when facing internal problems. Finally, insider CEOs have stronger relationships with major

customers than outsider CEOs. This stronger relationship with customers is likely to result in fewer

unanticipated shocks in demand that lead to unforeseen shocks in earnings.

Because internally-promoted CEOs have more in depth knowledge about the firm they

manage than outsider CEOs, we predict that these internally-promoted CEOs are more likely to

issue voluntary management forecasts than outsider CEOs. We also predict management forecasts

provided by internally-promoted CEOs will be more accurate than those provided by outsider

CEOs. This leads to our first two hypotheses:

Hypothesis 1: Internally-promoted CEOs are more likely to issue managerial forecasts

than outsider CEOs.

Hypothesis 2: Internally-promoted CEOs issue more accurate forecasts than outsider

CEOs.

We use two measures of CEO internal experience: A dummy variable that equals one if

the CEO is an outsider and zero otherwise (Outsider), and our hand-collected internal experience

variable (CEOExp). An important feature of succession origin is that it is easily observable by both

investors and analysts. Anecdotal evidence suggests that both firms and the media emphasize the

origin of the CEO at the time of hire. For example, a recent Wall Street Journal article5 states that

the board’s decision to appoint Douglas McMillon, “a long-serving insider” and “tried-and-true

company man,” represents “a strong signal that the retailer is unlikely to steer itself too far from

its course as it adjusts to the new realities of retailing.” The board ended up choosing Mr. McMillon,

the insider, over his main contender, Mr. Simon, “a straight-talking outsider.” Because investors

can easily gather a CEO’s succession origin, it should be the case that investors could foresee that

internally-promoted CEOs are more likely to issue more accurate forecasts than outsider CEOs.

5 Banjo, WSJ, November 25, 2013.

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Holthausen and Verrecchia (1988) argue that the stock price response to news increases with the

precision of the news. This finding suggests that if investors perceive internally-promoted CEOs’

management forecasts as more precise than those of outsider CEOs, their reaction to these forecasts

will be stronger. This leads to our last hypothesis:

Hypothesis 3: The market reacts more strongly to news in managerial forecasts of

internally-promoted CEOs than to outsider CEOs.

3. Data and methodology

3.1 Sample selection and key variables of interest

Panel A of Table 1 provides information about our sample selection procedure. Our initial

sample consists of 71,742 firm-quarter observations from the Compustat Quarterly database

(covering firms from the U.S.) merged with our hand-collected CEO internal experience data for

the period 2001-2011. We start in 2001 because of the U.S. adoption of Regulation Fair Disclosure

(Reg FD) in that year, and because FirstCall data is limited before 2001 (Chuk, Matsumoto and

Miller (2013). We end our hand collection in 2011 because FirstCall ends its data availability in

June 2011.

We lose 17,051 firm-quarter observations because we require firms to have at least twelve

quarters of lagged performance data and 24 consecutive lagged monthly returns to obtain standard

deviations of ROA and stock returns. Thus, our first valid observation is in 2001. We then lose

10,604 firm-quarter observations because of missing First Call actual earnings and analyst

forecasts. Finally, we lose 3,955 firm-quarter observations with missing control variables. This

yields a sample of 37,625 firm-quarter observations for the likelihood of issuing management

forecast tests (H1). The final sample consists of 11,184 firm-quarter observations with non-missing

management forecasts that are needed to test forecast accuracy (H2). The sample for testing

hypothesis 3 consists of 10,726 firm-quarter observations with non-missing three-day market

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adjusted stock returns. Below we describe the construction of the outsider CEO dummy variable,

CEO internal experience, and our management forecast data. Definitions of all our variables are

provided in Appendix A.

3.1.1. Characteristics of management forecasts

As mentioned above, we collect our sample of management forecasts from the First Call

database of Company Issued Guidance (CIG). We identify a sample of quarterly management

earnings forecasts for the years 2001–2011 and only include quantitative management earnings

forecasts such as point and range forecasts. When the forecast provides a range, we use the mid-

point of the range as the management forecast. We study both short-term and long-term

management forecasts in our analysis because it is unclear whether CEO internal experience

should matter more based on the horizon of a forecast. In Panel B of Table 1 we summarize the

incidence of management forecasts in our sample over time. Overall, managers issue forecasts

about 29 percent of the time.

*** Insert Table 1 here ***

3.1.2. CEO internal experience

As discussed above, our sample is restricted to U.S. firms covered in the Execucomp

database between 2001 and 2011. The Execucomp sample covers firms in the U.S. Standard and

Poor’s (S&P) 1,500 index. Because firms in the S&P 1,500 are large, they are also more complex,

which is where CEO internal experience is likely more important. We have two measures of CEO

internal experience. First, we use an indicator variable equal to 1 if the CEO is hired from outside

of the firm (Outsider) and zero otherwise. This CEO outsider indicator variable does not

distinguish between a manager who spent only one year in the firm before becoming CEO and a

manager who spent 20 years in the firm before becoming CEO. Both of these CEOs are insiders.

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Our theory suggests that the CEO with 20 years of experience in the firm before becoming CEO

should provide higher quality earnings forecasts than the CEO with only one year in the firm before

becoming CEO. Therefore, we use a second variable (CEOExp) to better capture the difference

between these two insider CEOs. We quantify CEO internal experience as the number of years

that an incoming CEO worked in the firm before becoming CEO (i.e., pre-CEO tenure). To

construct CEO internal experience, we compare the date that the individual joined the firm to the

date that the same individual becomes CEO. We begin this process by first selecting companies

from the Execucomp sample.

The Execucomp database provides the date that the CEO joined the company and the date

that the CEO became CEO. However, prior research finds that Execucomp has some data integrity

issues (e.g., Cadman et al. 2016), and consistent with this concern we find that the observations

that come from Execucomp have problematic entries. For example, the data in Execucomp

suggests that CEOs joined the company after they became CEO for some observations – which

clearly cannot be the case. Furthermore, the Execucomp data suggests that some individuals

became CEO one year after the CEO turned over. Because of these issues as well as the poor

coverage of internal experience within Execucomp, we manually search through proxy filings and

Forbes’ executive profiles. We also make the following adjustment: if the manager works for a

related predecessor firm, we change the date that the manager joined the company to the date that

she joined the predecessor firm.6 Thus, it is possible that our internal experience measure exceeds

the age of the current company.

Table 2 presents summary statistics of all our variables. In our data, CEOExp averages

about 7.9 years (or 11.4 if we exclude outsider CEOs). Consistent with Kaplan and Minton (2012),

6 Our results are not sensitive to this design choice. However, we believe this improves the accuracy of our hand-collected proxy

for CEO “insiderness” which can be used in future research. We are happy to provide this measure to other researchers upon request.

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about 31 percent of the sample is composed of outsiders (CEOExp = 0). The 90th percentile of

CEOExp is 26 years. Our outsider dummy variable can easily be derived from the CEO internal

experience variable because outsider CEOs have zero years of experience at the firm before

becoming CEO.


3.1.3. Firm characteristics, executive compensation and CEO characteristics

After collecting our management forecast data and the CEO internal experience measures,

we collect firm characteristics from Compustat. Compensation data and executive characteristics

variables come from Execucomp. Summary statistics of these variables are also provided in Table

2. Among the firm characteristics, the natural logarithm of assets averages 7.7 in our sample, which

is similar to that reported in Hilary and Hsu (2011) and Hribar and Yang (2016), but slightly larger

than reported in Baik, Farber and Lee (2011). Firms have a 1.7 percent standard deviation of

earnings and 12 percent return volatility, which is similar to that reported in Hribar and Yang

(2016). We observe years in which a firm reports a loss in 16 percent of our sample, which is

slightly more than in Hribar and Yang (2016), but their sample ends before the financial crisis

while our sample ends in 2011. Institutional ownership averages about 75 percent, which is again

similar to that reported in Hribar and Yang (2016). About 19 percent of the firms in our sample

are in the HighTech industry, which is similar to results reported in Baik, Farber and Lee (2011).

Overall, Table 2 provides evidence that summary statistics for our sample are in line with those in

related prior research.

3.1.4. Market reaction to management forecasts

The First Call CIG database includes the date of issue of the management forecasts. We

use this date to estimate the market reaction to management forecast issuances. Following

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Campbell, Dhaliwal, and Schwartz (2010), Baik, Farber, and Lee (2011), Gong, Li, and Zhou

(2013), we use standard event study methodology to estimate Cumulative Abnormal Returns (CAR)

to management forecasts from days t - 1 to t + 1 (CAR(-1, +1)), where day t is the day that

management issued their forecast. We use the market model to estimate beta in the pre-event

window (the pre-event window spans days t - 265 to t – 10 where day t is the date of the forecast).

We then calculate abnormal returns (ARs) on days t + j for a variety of j days around the forecast

day as the return on the company that day minus the expected return that day, as follows:

𝐴𝑅𝑖𝑡 = 𝑅𝑒𝑡𝑢𝑟𝑛𝑖𝑡 − 𝐵𝑒𝑡𝑎𝑖 𝑥 𝑀𝑎𝑟𝑘𝑒𝑡 𝑟𝑒𝑡𝑢𝑟𝑛𝑡 (1)

We then estimate Cumulative Abnormal Returns (CARs) around the forecast

announcement for each stock for the event window t - 1 to t + 1 as the sum of ARs for days t - 1,

t, and t + 1. For the analysis on the market reaction to news in forecasts (H3), we measure the news

revealed in the forecast as the difference between the management forecast (for the following

quarter) and the most recent analyst consensus forecast (also for the following quarter), scaled by

the stock price at the beginning of the current quarter (as in Baginski, Conrad, and Hassell, 1993).

3.2. Research design

We use three different models to test our three hypotheses. In the first model, we test

whether the incidence of voluntary disclosures is a function of CEO internal experience (H1). To

do this, we estimate logit regressions where our dependent variable is an indicator variable equal

to one if the firm provides a management forecast. Because we also focus on both short and long

horizon forecasts, we use three different disclosure indicator variables: A long horizon disclosure

indicator variable (MF_LHRZ) that equals one if the firm issued a forecast that given quarter such

that the forecast period is more than 60 days away from the date of issuance and zero otherwise, a

short horizon disclosure indicator variable (MF_SHRZ) that equals one if the firm issued a forecast

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that given quarter such that the forecast period is less than 60 days away from the date of issuance

and zero otherwise, and an indicator variable (MF) that equals one for firm-quarters in which

managers issue at least one earnings forecasts during the fiscal quarter and zero otherwise. Below

is our first model that tests H1:

Pr(𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝑖𝑡 = 1) = 𝑙𝑜𝑔𝑖𝑡(𝑐 + 𝛽1𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟𝑖𝑡(𝐶𝐸𝑂𝐸𝑥𝑝𝑖𝑡) + 𝛽2𝐸𝑎𝑟𝑙𝑦𝑇𝑒𝑛𝑢𝑟𝑒𝑖𝑡 +𝛽3𝐶𝐸𝑂𝐴𝑔𝑒𝑖𝑡 + 𝛽4𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽5𝐵𝑇𝑀𝑖𝑡 + 𝛽6𝑆𝑡𝑑𝑅𝑂𝐴𝑖𝑡 + 𝛽7𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + 𝛽8𝐿𝑜𝑠𝑠 +𝛽9𝑁_𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑖𝑡 + 𝛽10𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝐸𝑟𝑟𝑖𝑡 + 𝛽11𝐸𝑛𝑡𝐶𝑜𝑠𝑡𝑖𝑡 + 𝛽12𝐴𝑑𝑗𝑅𝑂𝐴𝑖𝑡 + 𝛽13𝑆𝑡𝑜𝑐𝑘𝐶𝑜𝑚𝑝𝑖𝑡 +𝛽14𝑂𝑝𝑡𝑖𝑜𝑛𝐶𝑜𝑚𝑝𝑖𝑡 + 𝛽15𝐶𝐸𝑂𝑂𝑤𝑛𝑖𝑡 + 𝛽16𝐼𝑛𝑠𝑡𝑂𝑤𝑛𝑖𝑡 + 𝛽17𝐻𝑖𝑔ℎ𝑇𝑒𝑐𝑖𝑡 + 𝛽18𝑅𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖𝑡 +𝑄𝑈𝐴𝑅𝑇𝐸𝑅_𝐹𝐸𝑡 + 𝑌𝐸𝐴𝑅_𝐹𝐸𝑡) (2)

Our control variables follow the prior literature and are based on those factors that are

believed to affect whether management issues a management forecasts (Baginski and Hassell,

1997; Hirst, Koonce, and Venkataraman, 2008; Hilary and Hsu, 2011; Gong, Li, and Zhou, 2013;

among others). For example, market participants have a greater appetite for forecasts when there

is high information asymmetry. Thus, we control for information asymmetry with a technology

indicator variable (HighTec) following Gong, Li, and Zhou (2013) and with firm size (Size). We

predict that high technology firms and large firms are more likely to issue forecasts than their

counterparts. Analysts and institutions tend to push firms to disclose forecasts, so firms with more

analyst coverage (N_Analysts) and higher institutional ownership (InstOwn) should be more likely

to issue forecasts and thus we predict coefficients on N_Analysts and InstOwn to be positive.

Litigation risk is also a significant determinant of voluntary disclosures (Brown, Hillegeist, and

Lo 2005). We control for litigation risk with a high-tech industry dummy variable (HighTech),

firm size (Size), variability in returns (StdRet), variability in ROA (StdROA) and a loss indicator

variable (Loss), following Gong, Li and Zhou (2013). Because managers are more likely to issue

forecasts when they are more useful to analysts (Gong, Li and Zhou 2013), we control for the

analyst forecast error (ForecastErr) in our regressions. We also control for potential proprietary

16

costs of releasing voluntary disclosures with the book to market ratio (BTM) in a similar way to

Ajinkya, Bhojraj, and Sengupta (2005) and with a proxy for entry costs (EntCost), following Gong,

Li and Zhou (2013). We expect firms with high proprietary costs of disclosure (firms with low

book to market ratios) are less likely to issue management forecasts than firms with low proprietary

costs of disclosure.

Executive compensation is also shown to drive voluntary disclosures. For example, Nagar,

Nanda, and Wysocki (2003) find that equity compensation is associated with more voluntary

disclosures. For this reason, we control for stock (StockComp), option compensation (OptionComp)

and CEO ownership (CEOOwn) in our analysis. Finally, we control for CEO age (CEOAge) and a

CEO early tenure indicator variable (EarlyTenure) following Gong, Li, and Zhou (2013) to

distinguish our variables of interest (Outsider and CEOExp) from other potentially confounding

CEO characteristics.7 We predict that older CEOs are less interested/comfortable in disclosing to

shareholders because they are more used to working in times when shareholders did not expressly

demand as much disclosure as they do today. CEOs may be under more pressure to disclose early

in their tenure. Alternatively, investors may want to give the CEOs some time to adapt to the new

firm before expecting voluntary disclosures. Because voluntary disclosures were less common

before the 1990s, it is likely that older CEOs have less experience (and interest) in issuing forecasts.

Finally, we include a regulated industry dummy variable because of evidence in Kasznik and Lev

(1995) that firms in regulated industries tend to issue fewer management forecasts. We include

quarter and year fixed effects in all our models. In addition, we use White (1980) standard errors

clustered at the firm level to control for heteroscedasticity as well as any serial dependence of error

7 Including CEO tenure instead of the EarlyTenure dummy variable does not affect our results. However, CEO tenure is not

significant when we include both CEO tenure and CEO internal experience, which is why we chose to include the EarlyTenure

dummy variable instead.

17

terms. To confirm our hypothesis 1 (H1), we expect to find a negative coefficient on the CEO

outsider dummy variable and a positive coefficient on CEOExp. Such a pattern of results would

be consistent with H1 and suggest that internally-promoted CEOs are more likely to issue forecasts

than outsider CEOs, and that this relation varies directly with the number of years the manager

worked for the firm prior to being promoted to CEO.

To test our second hypothesis (H2), we estimate regressions of forecast accuracy against

the same determinants of management forecasts identified in Equation (2).8 As discussed earlier,

we measure forecast accuracy as the scaled difference between the management forecast and actual

earnings. Consistent with our tests of Equation (2), we estimate regressions for long horizon

accuracy, short horizon accuracy, and a combined short/long horizon accuracy. Because our

dependent variable is the negative of the ex-post error in the forecast, we expect to find a negative

coefficient on the CEO outsider dummy variable and a positive coefficient on our CEOExp

variable. Such findings would be consistent with H2 and suggest that internally-promoted CEOs

provide more accurate forecasts than outsider CEOs, and that this relation directly varies with the

number of years the manager worked for the firm prior to being promoted to CEO.

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦𝑖𝑡 = 𝑐 + 𝛽1𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟𝑖𝑡(𝐶𝐸𝑂𝐸𝑥𝑝𝑖𝑡) + 𝛽2𝐸𝑎𝑟𝑙𝑦𝑇𝑒𝑛𝑢𝑟𝑒𝑖𝑡 + 𝛽3𝐶𝐸𝑂𝐴𝑔𝑒𝑖𝑡 +𝛽4𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽5𝐵𝑇𝑀𝑖𝑡 + 𝛽6𝑆𝑡𝑑𝑅𝑂𝐴𝑖𝑡 + 𝛽7𝑆𝑡𝑑𝑅𝑒𝑡𝑖𝑡 + 𝛽8𝐿𝑜𝑠𝑠 + 𝛽9𝑁_𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑖𝑡 +𝛽10𝐹𝑜𝑟𝑒𝑐𝑎𝑠𝑡𝐸𝑟𝑟𝑖𝑡 + 𝛽11𝐸𝑛𝑡𝐶𝑜𝑠𝑡𝑖𝑡 + 𝛽12𝐴𝑑𝑗𝑅𝑂𝐴𝑖𝑡 + 𝛽13𝑆𝑡𝑜𝑐𝑘𝐶𝑜𝑚𝑝𝑖𝑡 +𝛽14𝑂𝑝𝑡𝑖𝑜𝑛𝐶𝑜𝑚𝑝𝑖𝑡 + 𝛽15𝐶𝐸𝑂𝑂𝑤𝑛𝑖𝑡 + 𝛽16𝐼𝑛𝑠𝑡𝑂𝑤𝑛𝑖𝑡 + 𝛽17𝐻𝑖𝑔ℎ𝑇𝑒𝑐𝑖𝑡 +𝛽18𝑅𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝑄𝑈𝐴𝑅𝑇𝐸𝑅_𝐹𝐸𝑡 + 𝑌𝐸𝐴𝑅_𝐹𝐸𝑡 (3)

Finally, to test our third and final hypothesis (H3), we estimate regressions of the stock

8 An alternative explanation is that internal experience provides the CEO with a greater opportunity to manage earnings, thus

making them appear to provide more accurate forecasts. For these reasons, it might seem reasonable that we include a control for

earnings management activities. We do not do this in our main model in order to maintain consistency between tests of H1 and H2,

and also because prior research suggests that, if anything, external CEOs engage in greater levels of earnings management than

internal CEOs (Kuang, Qin, and Wielhouwer 2014). Nevertheless, in untabulated results, we also include a control for earnings

management activities using the unsigned discretionary accruals following the modified Jones model (Dechow et al. 1995). All of

our results remain, suggesting that our results are not likely explained by managers appearing to be more accurate in their forecasts

because their internal experience allows them to more easily manage earnings to their forecasts.

18

market reaction to management forecasts (CAR(-1,+1)) against the news revealed by the forecast and

the interaction of the news revealed and our CEO internal experience variable.

𝐶𝐴𝑅(−1,1) = 𝑐 + 𝛽1𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟𝑖𝑡(𝐶𝐸𝑂𝐸𝑥𝑝𝑖𝑡) + 𝛽2 𝑂𝑢𝑡𝑠𝑖𝑑𝑒𝑟𝑖𝑡(𝐶𝐸𝑂𝐸𝑥𝑝𝑖𝑡)𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 +𝛽3𝑁𝑒𝑤𝑠 𝑖𝑡 + 𝛽4𝐸𝑎𝑟𝑙𝑦𝑇𝑒𝑛𝑢𝑟𝑒 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 + 𝛽5𝐶𝐸𝑂𝐴𝑔𝑒 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 + 𝛽6𝑆𝑖𝑧𝑒 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 +𝛽7𝐵𝑇𝑀 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 + 𝛽8𝑆𝑡𝑑𝑅𝑂𝐴 𝑥 𝑁𝑒𝑤𝑠 + 𝛽9𝐻𝑜𝑟𝑖𝑧𝑜𝑛 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 +𝛽10𝑀𝐹𝑙𝑜𝑠𝑠 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 + 𝛽11𝑃𝑜𝑖𝑛𝑡 𝑥 𝑁𝑒𝑤𝑠𝑖𝑡 + 𝛽12𝐸𝑎𝑟𝑙𝑦𝑇𝑒𝑛𝑢𝑟𝑒𝑖𝑡 + 𝛽13𝐶𝐸𝑂𝐴𝑔𝑒𝑖𝑡 +𝛽14𝑆𝑖𝑧𝑒𝑖𝑡 + 𝛽15𝐵𝑇𝑀𝑖𝑡 + 𝛽16𝑆𝑡𝑑𝑅𝑜𝑎𝑖𝑡 + 𝛽17𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑖𝑡 + 𝛽18𝑀𝐹𝑙𝑜𝑠𝑠𝑖𝑡 +𝑄𝑈𝐴𝑅𝑇𝐸𝑅_𝐹𝐸𝑡 + 𝑌𝐸𝐴𝑅_𝐹𝐸𝑡 (4)

If investors understand that internally-promoted CEOs are likely to provide higher quality

management forecasts than outsider CEOs (i.e., H3), we should find a negative coefficient on the

interaction between the CEO outsider dummy variable and News. Similarly, if the number of years

that a CEO worked for the firm prior to becoming CEO affects the quality of management forecasts,

we should find a positive coefficient on the interaction between CEOExp and News.

Equation (4) includes control variables following extant studies (see, for example, Gong,

Li, and Zhou, 2013 and Hilary and Hsu, 2011, among others) and interactions of all our control

variables with the News variable. For our control variables, we expect a more positive stock price

reaction (regardless of the news revealed in the forecast) when there is more uncertainty in the

firm because investors discount stock prices of firms with high uncertainty. Thus, we expect a

more positive stock market reaction to the issuance of forecasts of smaller firms (SIZE), higher

book to market ratios (BTM), and higher ROA volatility (STDROA). We also expect that the stock

price reaction to management forecasts is positively related to the surprise in the forecast (NEWS)

and negatively related to the disclosure of an expected loss in future earnings (MFLOSS).9

4. Results

9 Prior research points out that the market reaction to management forecasts is difficult to interpret if the forecast is issued at the

same time as a current earnings announcement (i.e., “bundled” forecasts) (Rogers and Van Buskirk 2013). To ensure our results

are not driven by market reactions to concurrently released earnings announcements, we re-examine tests of H3 only on a subset

of “unbundled” forecasts. Our results are unchanged.

19

We now turn to our empirical tests of our hypotheses. First, we present univariate tests of

our hypotheses with correlation tables. Then, we present results of our multivariate analysis.

4.1. Univariate results

In Table 3, we summarize the pairwise correlations between our variables of interest for

the largest sample in our paper (including firm quarter observations with and without a forecast).

This sample is used to test our first hypothesis that insider CEOs are more likely to issue

management forecasts. The results are the same if we instead focus on the other samples used in

our paper.

In Panel A, we present correlations for insider CEOs and outsider CEOs, whereas we limit

the sample to insider CEOs in panel B. We present Pearson correlation coefficients in the upper

triangle and Spearman correlation coefficients in the lower triangle in Panels A and B. MF is an

indicator variable that equals one if the firm issued a forecast that quarter and zero otherwise.

Univariate correlations suggest that riskier firms (firms with high standard deviations of earnings

and returns) are more likely to issue forecasts than safer firms. Also, firm performance (as

measured by industry-adjusted ROA and the loss indicator variable) is positively related to issuing

a forecast. Smaller firms in the technology sector likely have higher uncertainty and are associated

with a higher incidence of forecasts than larger firms that are not in the technology sector. Higher

levels of option-based compensation (OptionComp) is also associated with higher incidence of

management forecast disclosure. CEOs are less likely to issue forecasts in the first three years of

their tenure as CEOs (perhaps when career concerns are greatest). However, our variables of

interest in H1 and H2 – the CEO outsider indicator variable and the years of inside experience of

internally-promoted CEOs in the firm before becoming CEOs (CEOExp) – are both unrelated to

the likelihood of issuing a forecast. One possible explanation for the lack of relationship between

20

the variables is that large firms tend to hire CEOs with high internal experience. So, it is possible

that insider CEOs are more likely to issue a forecast than outsider CEOs after controlling for the

confounding effect of firm size. The multivariate analyses that follow allow us to verify this

possibility. Finally, an important observation from Table 3 is that the results do not differ between

Pearson and Spearman correlations. This result suggests that any results we find in multivariate

analyses are unlikely to be due to outlier observations.


4.2 Multivariate results

We present results of our multivariate tests of our first hypothesis H1 in Table 4.

Specifically, we test whether CEO internal experience is related to the incidence of management

forecasts. As discussed earlier, we present three different analyses of Hypothesis 1 depending on

the forecast horizon. In Panel A, we present results for managers’ propensity to issue earnings

forecasts. That is, the dependent variable is one for firm-quarters in which managers issue at least

one earnings forecasts during the fiscal quarter, and zero otherwise. In Panels B and C, we examine

whether CEO internal experience (CEOExp) differentially affects managers’ decision to issue

long-horizon versus short-horizon earnings forecasts. As previously discussed, we estimate logit

regressions in this analysis. Therefore, we present regular coefficients in the first column of each

model and marginal coefficients (holding all other variables at their mean) in the second column

of each model. In model 1, the variable of interest is the outsider CEO dummy variable (Outsider).

In model 2, we restrict the sample to insider CEOs and the variable of interest is the degree of CEO

insiderness (CEOExp).

Consistent with univariate analysis, we find that smaller, well performing firms (as

measured by the Loss dummy variable) are more likely to issue forecasts than their counterparts.

21

However, riskier firms are less likely to issue forecasts than safer firms. In addition, younger

managers who are paid with more stock and option compensation are more likely to issue forecasts

than older managers who receive more cash-based compensation. Firms with more analyst

coverage and institutional ownership are also more likely to issue managerial forecasts. Thus,

coefficients on our control variables are fairly consistent with theory and extant work.

Turning to our variables of interest, we first find that outsider CEOs are less likely to issue

earnings forecasts. Similarly, in the sample of insider CEOs, CEO internal experience is positively

related to the incidence of managerial forecasts (the coefficients on the CEO outsider dummy

variable and on CEO internal experience are statistically significant at the 1% level). These results

are consistent with H1, and suggest that internally-promoted CEOs are more likely to issue

management forecasts than outsider CEOs, and that this relation is directly related to the number

of years the manager worked at the firm prior to becoming CEO.

These results are not only statistically significant, but they are also economically significant.

Specifically, Outsider CEOs are 1.2 percent less likely to issue management forecasts than insider

CEOs, which is economically significant given that only 29 percent of firms issue a forecast in a

given quarter in our sample. That is, a 1.2 percentage point decrease translates to a 4.1 percent

decrease in the likelihood that management issues a forecast. Turning to the sample of insider

CEOs (CEOExp), moving from the first to the third quartile of the distribution of CEO internal

experience (13 years) leads to a 2 percentage point increase in the likelihood of issuing a forecast,

which equates to a 6 percent increase in the mean likelihood of issuing a forecast. However, when

we split into short and long-horizon forecasts, we find that outsider CEOs are only less likely to

issue short-horizon forecasts. Internal experience, however, is positively related to the likelihood

of issuing both short and long-term forecasts. Overall, the results presented in Table 4 are

22

consistent with H1 and suggest that internally-promoted CEOs are consistently more likely to issue

earnings forecasts than outsider CEOs, and that this relation varies with the number of years the

manager worked at the firm prior to being promoted to CEO.


Next, we turn to the accuracy of managerial forecasts to test our second hypothesis H2.

Here, we estimate the accuracy of each forecast – conditional on issuing an earnings forecast. We

present the results of this analysis in Table 5. The dependent variable in Panel A is the accuracy

of all earnings forecasts. In Panel B, the dependent variables are the accuracy of long and short

horizon forecasts. As previously discussed, accuracy is measured as the negative of the absolute

value of the deflated difference between actual earnings and the management earnings forecast.

Therefore, higher values of the accuracy variable correspond to higher accuracy. In model 1 of

Panel A, the sample includes both internally-promoted and outsider CEOs, whereas the sample is

limited to internally-promoted CEOs in model 2 of Panel A. Because we predict that internally-

promoted CEOs issue more accurate forecasts than outsider CEOs, we should find a negative

coefficient on the outsider CEO dummy variable and a positive coefficient on CEO internal

experience. Accuracy averages -0.004 in our sample. Coefficients for this regression are naturally

small because accuracy is small. For readability, the coefficients in tables with forecast accuracy

results are multiplied by 1,000.

Focusing on our control variables, we observe that forecast accuracy is negatively related

to firm risk (StdROA and StdRet). Older CEOs tend to make worse forecasts than younger CEOs

and equity-based compensation leads to more accurate forecasts. There is weak evidence that

larger firms tend to issue less accurate forecasts than smaller firms, which is also consistent with

Baik et al. (2011) and Hribar and Yang (2016).

23

Turning to our variable of interest, consistent with H2, we observe that outsider CEOs

provide less accurate earnings forecasts than internally-promoted CEOs. Similarly, the number of

years the manager worked at the firm prior to becoming CEO (i.e., CEO internal experience) is

positively related to accuracy. The coefficient on our outsider indicator variable is approximately

-0.43. However, coefficients in Table 5 are multiplied by 1,000, which means that the effect of

CEO internal experience on accuracy is really -0.43 divided by 1,000, or -0.0004 (10 percent of

forecast accuracy mean in our sample).10 Alternatively, the coefficient on CEO internal experience

is about 0.037, which implies that moving from the first to the third quartile of CEO internal

experience (13 years) leads to an improvement in accuracy of 0.48. Dividing 0.48 by 1000, we get

a raw effect of 0.00048, which is about 12 percent of the mean accuracy in our sample. Thus,

spending more years in the firm before becoming CEO helps the CEO make more accurate

forecasts.

Results of our accuracy analyses are similar when we separate the sample into short and

long-horizon management forecasts (Panel B of Table 5). As before, we first use the whole sample

(models 1 and 2) and we exclude outsiders in the second set of analyses (models 3 and 4). In

models 1 and 3, the dependent variable is the accuracy of short-horizon forecasts. In models 2 and

4, the dependent variable is the accuracy of long-horizon forecasts. The first notable result is that

forecasts of outsider CEOs are less accurate than those of internally-promoted CEOs in both short

and long-term forecasts.

In models 3 and 4 of Panel B of Table 5, we limit our sample to insider CEOs and re-test

whether forecast accuracy is a function of the number of years the manager worked at the firm

prior to becoming the CEO. This time, our variable of interest is CEOExp. Consistent with our

10 This is estimated as 0.0004 divided by a mean value of forecast accuracy of -0.004.

24

theory, we find that more internal experience (i.e., higher CEOExp) is associated with more

accuracy with both short and long-term horizon forecasts. There results suggest that CEOExp

contains more information about CEO internal experience than the outsider CEO dummy variable.

In addition, our results confirm that internal experience and knowledge of the firm helps CEOs

make more accurate earnings forecasts.


Given that CEO internal experience leads to higher incidence and more accurate

management forecasts, the next question is whether the market incorporates this information in

their trading. To test this third hypothesis H3, we estimate regressions of the market reaction to

management forecasts against the interaction between internal experience and the news revealed

in the forecast. More positive news should lead to a more positive reaction to the management

forecasts. However, forecasts are not actual earnings and some managers make better forecasts

than others. H2 suggests that internally-promoted CEOs issue more accurate forecasts than

outsider CEOs. Therefore, a positive forecast release should lead to a more positive stock price

reaction for internally-promoted CEOs than for outsider CEOs. Thus, we are interested in the

coefficient on the interaction between internal experience (Outsider and CEOExp) and the news

revealed by the forecast.

Because we predict that the market should react more strongly to news revealed in

management forecasts of internally-promoted CEOs, we expect to find a negative coefficient on

the interaction between the Outsider indicator variable and the News variable. For this analysis,

we need to be able to estimate abnormal market reactions to management forecasts, which limits

the sample size further from the sample presented in Table 5. We present summary statistics of the

variables needed to test H3 in Panel A of Table 6.

25


The mean reaction (CAR (-1, +1)) to forecast announcements is about 0.1 percent, which

is essentially zero given the standard deviation of 9 percent. This on average result of zero is

reasonable given that some management forecasts provide good news and others provide bad news.

Correspondingly, our News variable is also very small with a mean of -0.3 percent and a standard

deviation of 5.6 percent. All other firm and executive characteristics are roughly similar to those

summarized in Table 2.

In Panel B of Table 6, we present regression results of our analysis of hypothesis 3. The

coefficient on News is positive and significant at the 1 percent level. More importantly, the

coefficient on the interaction between the Outsider dummy variable and News is negative and

significant, as expected. This result is consistent with H3 and suggests that investors recognize the

value of internal experience on the quality of managerial forecasts. Further, this result extends to

the number of years that the manager worked at the firm prior to being promoted to CEO.

Specifically, the coefficient on the interaction between news and CEOExp is positive and

statistically significant.

5. Alternative explanations, robustness tests, and a discussion of causality

In our main tests, we identify and control for alternative explanations for our empirical

findings. Specifically, we control for the possibility that poor firm governance leads to external

CEO turnover as well as to poor reporting quality (through control variables related to analyst

coverage, institutional ownership, among others). We also control for the possibility that firm

performance is simply harder to predict and this leads to a greater likelihood of external CEO

turnover as well as poor reporting quality (with stock return volatility and profitability volatility).

Finally, we control for the possibility that the new CEO’s risk aversion leads to more truthful

26

reporting and that this risk aversion is somehow correlated with being promoted internally (with

compensation features, age, and tenure).

In additional analyses, we identify two additional alternative explanations for our findings

and design tests to mitigate the likelihood that they are driving our results. First, we discuss how

firm performance prior to CEO turnover could be poor, and this could simultaneously lead to a

deterioration of financial reporting quality as well as a replacement of the CEO from outside the

firm. Second, we discuss that there could be additional personal characteristics of the CEO that

are correlated with prior work experience but lead the manager to be of higher quality and also

produce higher quality financial reports.

5.1. Pre-turnover performance

A large body of literature shows that pre-turnover performance is a significant determinant

of CEO internal experience.11 In particular, poorly performing firms are more likely to replace a

CEO with an outsider, whereas well performing firms are likely to replace a CEO with an insider.

This happens because poorly performing firms need change and insiders may not be capable of

producing such change. We already showed that performance is negatively related to both the

likelihood of issuing forecasts and the accuracy of forecasts. Therefore, it is possible that firms

with poor performance, who tend to have outsider CEOs also have fewer and less accurate

forecasts.

So far, we address this concern in a couple of ways. First, we control for performance (with

Loss and industry adjusted ROA) in all our analyses. Second, we always perform our analysis in

the sample of insider CEOs and outsider CEOs and, separately, in the sample of insider CEOs only.

When we exclude outsider CEOs, we eliminate the 30 percent of firms that choose to hire an

11 Finkelstein, Hambrick, and Canella (2009) summarize some of this extensive literature.

27

outsider CEO. All CEOs in this subsample are insider CEOs and vary only in the degree of internal

experience. A third way to address the issue of pre-turnover performance is to control for firm

performance before turnover directly. In Table 7, we present results of analyses in which we re-

estimate our regressions and control for performance before turnover. In Panel A, we present

results in the sample that includes both insider and outsider CEOs. In Panel B, we restrict the

sample to include only insider CEOs. In model 1, we analyze the likelihood of providing a forecast.

The dependent variable in model 2 is forecast accuracy and the dependent variable in model 3 is

the stock price reaction to the issuance of forecasts.


Results with the additional pre-turnover performance control are consistent with our earlier

results. Regarding the likelihood of issuing forecasts, the outsider dummy variable is insignificant

but the coefficient on CEOExp is positive and significant. Thus, we continue to find support for

H1. Second, we again find that internally-promoted CEOs provide more accurate forecasts. The

outsider CEO dummy variable (Outsider) is negatively related to forecast accuracy and CEO

internal experience (CEOExp) is positively related to forecast accuracy. Finally, the market reacts

more strongly to news revealed in forecasts of internally-promoted CEOs (Outsider = 0) than to

news revealed in forecasts of outsider CEOs. As before, the coefficient on the interaction between

CEO internal experience and News is positive and significant at the 1 percent level. Overall, our

results continue to indicate that internally-promoted CEOs provide higher quality and more

accurate forecasts than outsider CEOs. In addition, the market recognizes that internally-promoted

CEOs provide more accurate forecasts.

5.2. CEO Ability

28

A possible explanation for our results is that CEO internal experience is another proxy for

CEO ability. We believe this explanation is unlikely for a number of reasons. Specifically, CEO

ability per se should be able to transfer from firm to firm. CEO internal experience, on the other

hand, does not transfer at all across firms. A high ability manager who becomes a CEO in a new

firm (as an outsider) gets CEO internal experience of zero. Second, it is not clear that insiders who

have spent their careers in a single firm would have the highest ability, while outsiders would

necessarily be the least capable CEOs. Nonetheless, to control for this possibility, we re-estimate

all our analysis controlling for CEO ability. One problem with this analysis is that CEO ability is

very difficult to measure. To be consistent with extant literature, we borrow the CEO ability from

Demerjian, Lev, and McVay (2012). We present the results of this analysis in Table 8. In short,

the results are very similar to our main results when we control for CEO ability. Insider CEOs (and

those with high internal experience) have a greater incidence and higher accuracy of management

forecasts. Finally, the market reacts more strongly to news in management forecasts of CEOs with

high internal experience than to CEOs with low internal experience. Thus, CEO internal

experience does not appear to be a proxy for CEO ability.


5.3 Discussion of causality

As with all empirical work and particularly in CEO turnover and succession research, our

tests represent associations for which we cannot definitively ascribe causality. We attempt to rule

out the possibility that our results are driven by a correlated omitted variable that simultaneously

leads to externally hired CEOs and poor financial reporting quality by controlling for several

factors (i.e., firm governance, pre-turnover performance, firm riskiness, and CEO characteristics

29

other than their prior work experience). In addition, we perform two additional sets of analysis

(untabulated, but available upon request to the interested reader) to mitigate endogeneity concerns.

First, we perform tests using propensity score matched samples. In particular, we first

generate an outsider CEO dummy variable (OUTSIDER) that is equal to one if CEO is an outsider

and zero, otherwise. Then, for each firm with an outsider CEO (treated firm) we identify a match

(based on determinants of management forecast issuances, management forecast accuracy, and the

stock price to management forecasts) with an insider CEO (control firm). For the matched sample

analyses, differences between matched pairs were evaluated using the signed rank test for

continuous data and the McNemar's test for binary data. There is no longer a significant difference

between the characteristics of the treatments and controls in sample. Within this propensity score

matched sample, we re-test all our hypotheses and confirm that our results remain strong.

Second, we also perform a two-stage least squares (2SLS) analysis. Specifically, in the first

stage, we first estimate a logit regression of succession origin against firm characteristics. In this

first stage, we obtain all independent variables in the first stage regression at the year before CEO

turnover year. The arguably exogenous variable is the ratio in the salary of the CEO to the second

in command. A higher salary ratio implies that the firm values the second in command highly and

so the firm should be less likely to hire an outsider CEO. This ratio in compensation between the

first and second in command before CEO turnover is unlikely to affect characteristics of forecasts

after the CEO turnover. Next, we generate predicted values of succession origin (PredOutsider).

This instrumented variable becomes our variable of interest in our second stage regressions (our

second stage regressions are forecast incidence, accuracy and the stock price reaction to forecast

issuance). All of our results hold using this alternative specification.

30

Finally, any alternative explanation to our results would require an association with

financial reporting quality in the exact same manner as CEO internal experience. Despite our

inability to identify any plausible such correlated omitted variable, it is possible that one exists and

we have not adequately controlled for it. Nevertheless, because our tests focus on the time periods

after the new CEO is hired, our findings imply that when a new CEO has internal experience that

firm’s financial reporting quality improves, regardless of the reason the new CEO is hired.

6. Conclusion

Internally-promoted CEOs should have a deeper understanding of their firm’s products,

supply chain, operations, business climate, corporate culture, and how to navigate among

employees to get the information they need. Thus, we argue that internally-promoted CEOs are

likely to produce higher quality financial reports than outsider CEOs. We hand-collect whether a

CEO is hired from inside the firm and, if so, the number of years they worked at the firm before

becoming CEO. We then examine whether managers with more internal experience issue higher

quality disclosures, and offer three main findings. First, CEOs with more internal experience are

more likely to issue voluntary earnings forecasts than those managers with less internal experience

as well as those managers hired from outside the firm. Second, the earnings forecasts issued by

CEOs with more internal experience are more accurate than those managers with less internal

experience as well as those managers hired from outside the firm. Finally, investors react more

strongly than to forecasts of insider CEOs to forecasts of outsider CEOs. Overall, our findings

suggest that when managers have work experience with the firm prior to taking the CEO position,

the firm’s financial reporting is of higher quality.

Future research may wish to re-examine prior results from the CEO turnover and

succession plan literature after considering the number of years of internal experience a CEO has

31

prior to taking office. Additionally, if internal experience improves financial reporting quality,

then these firms should also see reductions in the cost of capital and increases in investment levels.

Future research could examine whether this is in fact the case. As previously noted, our measure

for financial reporting quality is management forecasts, but there are other measures used in prior

literature such as restatements, internal control weaknesses, discretionary accruals, or other

disclosures from Forms 10-K and 10-Q. Future research may wish to examine whether our results

generalize to these alternative measures for financial reporting quality. We expect that they might

because a firm’s CEO signs certifications in these documents attesting to their accuracy (and

subjecting themselves to criminal and personal liability if they knowingly misrepresent their

financial position). Finally, a limitation of our study is that our sample only includes U.S. firms

that are subject to the specific legal, enforcement, and business environment of the U.S. Future

work may wish to examine whether our findings generalize to other countries.

32

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Appendix A: Variable Definitions

MF An indicator variable which equals one for firm-quarters in which managers issue

at least one earnings forecasts during the fiscal quarter, and zero otherwise;

MF_LHRZ An indicator variable which equals one for firm-quarters in which managers issue

long-horizon earnings forecasts during the fiscal quarter, zero otherwise. A long-

horizon earnings forecast is defined as a management earnings forecast issued

more than 60 days prior to the end of forecasting period;

MF_SHRZ An indicator variable which equals one for firm-quarters in which managers issue

short-horizon earnings forecasts during the fiscal quarter, zero otherwise. A short-

horizon earnings forecast is defined as a management earnings forecast issued

equal to or less than 60 days prior to the end of forecasting period;

Outsider An indicator variable which equals one if CEO is hired from outside the firm;

CEOExp The number of years the CEO has worked in the firm before becoming the CEO;

EarlyTenure An indicator variable which equals one if CEO tenure is equal to or less than three

years;

CEOAge The age of the CEO;

Size The natural log of total assets at the beginning of quarter t;

BTM Book-to-market, measured as book value of equity divided by market value of

equity at the beginning of quarter t;

StdROA Standard deviation of return on assets over the 12 quarters prior to quarter t;

StdRet Standard deviation of monthly raw stock returns over the 24 months prior to

quarter t;

Loss An indicator variable that equals one if net income for quarter t is less than zero,

and zero if reported net income for quarter t is greater than or equal to zero;

N_Analysts The natural log of the number of individual analyst’s forecasts in the most recent

analyst consensus;

ForecastErr Absolute value of the difference between quarter t + 1 actual earnings per share

and the most recent analyst consensus (median) forecast issued prior to quarter t

earnings announcement, scaled by the closing share price at the end of quarter t;

AdjROA Return-on-assets, measured as earnings before extraordinary item scaled by lagged

total assets, minus the median return-on-asset for the same two-digit SIC industry

for quarter t;

36

EntCost Industry-level weighted average gross cost of property, plant and equipment,

weighted by each firm’s market share (based on sales) in this industry;

StockComp CEO stock compensation divided by total compensation;

OptionComp CEO option compensation divided by total compensation;

CEOOwn The natural log of market value of CEO owned shares;

InstOwn The percentage of share outstanding held by institutional investors;

HighTech An indicator variable that equals one if the firm reports Compustat SIC codes

2833–2836 (Drugs), 8731–8734 (R&D services), 7371–7379 (Programming),

3570–3577 (Computers), or 3600–3674 (Electronics), and zero otherwise;

Regulation An indicator variable that equals one if the firm reports Compustat SIC codes

4812–4813 (Telephone), 4833 (TV), 4841 (Cable), 4811–4899 (Communications),

4922–4924 (Gas), 4931 (Electricity), 4941 (Water), or 6021–6023, 6035–6036,

6141, 6311, 6321, 6331 (Financial firms), and zero otherwise;

Accuracy The negative of the absolute value of the difference between forecast and realized

earnings, deflated by the stock price two days before the issuance of the

management forecast;

Horizon The number of days between the management earnings forecast issuance date and

the end of the fiscal year being forecasted;

CAR(-1, +1) Three-day cumulative market adjusted stock returns around the management

earnings forecast issuance date;

News The difference between point management forecasts or the mid-point of the range

management forecasts of quarter t + 1 earnings per share and the most recent

analyst consensus (median) forecasts of quarter t + 1 earnings per share made prior

to the management forecast issuance date, scaled by the quarter-beginning stock

price;

MFloss An indicator variable that equals one if a management earnings forecast is less than

zero, and zero if a management earnings forecast is greater than or equals to zero;

Point An indicator variable that equals one if the management earnings forecast is a point

forecast, zero if it is a range forecast;

MAScore Managerial ability measure by Demerjian et al. (2012);

PreturnoverROA Average industry adjusted return on assets over the two years before CEO turnover.

37

Table 1

Sample Selection

Panel A: Sample construction

Number of firm-quarter observations in Compustat Quarterly (2001-2011) with non-

missing CEO information (CEOExp, CEOAge, CEO Tenure)

71,742

Less: Missing standard deviation of previous 12-quarter return on assets 17,051

Missing standard deviation of previous 24-month returns 349

Missing actual earnings and consensus analyst forecast 10,604

Missing CEO compensation related variables (Stock_comp, Option_comp,

CEOOwn) 2,158

Missing other control variables 3,955

Final Sample for H1 (Number of firm-quarters) 37,625

Less: Missing management forecast accuracy 26,441


Less: Missing three-day cumulative market adjusted stock returns 458


Panel B: Sample distribution by fiscal year

Fiscal

Year N

Percentage of

quarters with

management

forecasts

Outsider CEO internal

experience

2001 2,836 37.13% 32.44% 11.79

2002 3,079 39.17% 32.05% 11.52

2003 3,421 36.19% 32.32% 11.52

2004 3,476 37.69% 30.63% 11.31

2005 3,677 33.02% 32.11% 11.42

2006 3,257 31.13% 31.11% 11.22

2007 4,291 29.64% 29.94% 11.58

2008 4,250 26.61% 31.69% 11.25

2009 4,215 22.61% 31.44% 11.31

2010 4,081 23.67% 30.91% 11.60

2011 1,082 24.49% 29.23% 11.56

Total 37,625 29.05% 31.04% 11.42

Table 1 describes our sample. Panel A outlines the sample selection criteria. Panel B reports the total

number of firm-quarters, the number and percentage of firm-quarters with management earnings forecasts,

and CEO internal experience over the sample period between fiscal year 2001 and fiscal year 2011.

38

Table 2

Summary statistics

Variable N Mean Std Dev P10 P25 Median P75 P90

MF 37,625 0.290 0.454 0.000 0.000 0.000 1.000 1.000

Accuracy 11,184 -0.004 0.008 -0.009 -0.004 -0.002 -0.001 0.000

Outsider 37,625 0.310 0.463 0.000 0.000 0.000 1.000 1.000

CEOExp 25,948 11.422 9.281 2.000 4.000 9.000 17.000 26.000

EarlyTenure 37,625 0.363 0.481 0.000 0.000 0.000 1.000 1.000

CEOAge 37,625 55.490 7.210 46.000 51.000 56.000 60.000 64.000

Size 37,625 7.705 1.638 5.696 6.515 7.574 8.748 9.965

BTM 37,625 0.522 0.394 0.168 0.283 0.446 0.663 0.937

StdROA 37,625 0.017 0.026 0.002 0.004 0.009 0.018 0.039

StdRet 37,625 0.121 0.065 0.057 0.077 0.105 0.146 0.206

Loss 37,625 0.157 0.364 0.000 0.000 0.000 0.000 1.000

N_Analysts 37,625 2.101 0.674 1.099 1.609 2.197 2.639 2.944

ForecastErr 37,625 0.005 0.026 0.000 0.000 0.001 0.004 0.009

EntCost 37,625 8.138 2.317 5.760 7.485 8.770 9.464 10.047

AdjROA 37,625 0.006 0.029 -0.012 -0.002 0.005 0.017 0.034

HighTech 37,625 0.185 0.389 0.000 0.000 0.000 0.000 1.000

Regulation 37,625 0.084 0.277 0.000 0.000 0.000 0.000 0.000

CEOOwn 37,625 8.873 1.881 6.634 7.758 8.861 9.969 11.195

StockComp 37,625 0.192 0.230 0.000 0.000 0.085 0.351 0.542

OptionComp 37,625 0.269 0.266 0.000 0.000 0.220 0.455 0.679

InstOwn (%) 37,625 75.106 19.770 49.037 63.998 77.822 88.475 97.093

Horizon 11,184 53.898 24.803 17.000 42.000 62.000 69.000 73.000

Table 2 presents descriptive statistics of variables used in our main tests. The sample period ranges from

2001 to 2011. See Appendix A for variable definitions. All variables are winsorized at top and bottom one-

percentiles except for Early, Loss, N_Analysts, HighTech, and Regulation. Variables StdRet, StdROA,

EntCost, CEOOwn, Stock_Comp, Option_Comp are winsorized at the top one-percentile only.

39

Table 3

Pearson and spearman correlations

Panel A: Whole Sample (including both insider and outsider CEOs)

VARIABLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)

(1) MF 1.000 0.000 -0.023 -0.059 -0.064 -0.108 0.003 0.024 -0.061 0.161 -0.073 0.059 0.059 0.112 -0.145 0.032 -0.037 0.133 0.125

(2) Outsider 0.000 1.000 -0.074 0.059 -0.187 0.007 0.118 0.181 0.076 -0.044 0.023 -0.005 -0.049 0.145 -0.043 -0.024 -0.024 0.013 0.012

(3) EarlyTenure -0.023 -0.074 1.000 -0.270 0.050 0.010 0.048 0.009 0.048 -0.012 0.015 0.013 -0.020 -0.008 -0.013 -0.303 0.045 0.053 -0.036

(4) CEOAge -0.063 0.042 -0.269 1.000 0.106 0.060 -0.084 -0.091 -0.052 -0.030 -0.012 -0.025 0.003 -0.102 0.033 0.248 -0.013 -0.114 -0.068

(5) Size -0.057 -0.183 0.048 0.105 1.000 0.038 -0.256 -0.398 -0.131 0.472 -0.033 -0.099 0.014 -0.179 0.261 0.323 0.232 0.000 -0.114

(6) BTM -0.123 -0.003 0.008 0.076 0.057 1.000 0.020 0.108 0.272 -0.229 0.263 -0.060 -0.325 -0.122 0.127 -0.211 0.035 -0.163 -0.048

(7) StdROA 0.056 0.140 0.055 -0.101 -0.373 -0.077 1.000 0.485 0.249 -0.060 0.113 0.131 -0.123 0.217 -0.084 -0.164 -0.071 0.079 -0.058

(8) StdRet 0.041 0.177 0.007 -0.091 -0.453 0.083 0.528 1.000 0.296 -0.097 0.118 0.061 -0.162 0.248 -0.173 -0.190 -0.193 0.183 -0.078

(9) Loss -0.061 0.076 0.048 -0.052 -0.129 0.220 0.286 0.274 1.000 -0.126 0.239 0.052 -0.512 0.096 -0.034 -0.204 -0.036 0.044 -0.072

(10) N_Analysts 0.154 -0.052 -0.010 -0.028 0.476 -0.275 -0.050 -0.083 -0.121 1.000 -0.113 0.064 0.137 0.118 -0.076 0.306 0.099 0.206 0.162

(11) ForecastErr -0.171 0.057 0.020 0.003 -0.080 0.339 0.221 0.190 0.293 -0.237 1.000 -0.011 -0.238 -0.018 0.012 -0.122 -0.002 -0.049 -0.059

(12) EntCost -0.020 -0.026 0.004 0.003 0.047 -0.016 0.179 -0.017 0.040 0.097 0.065 1.000 0.042 0.180 0.028 -0.045 0.072 0.031 0.072

(13) AdjROA 0.110 -0.035 -0.024 -0.007 -0.062 -0.469 0.007 -0.116 -0.525 0.183 -0.267 0.069 1.000 0.067 -0.063 0.167 -0.023 0.053 0.064

(14) HighTech 0.112 0.145 -0.008 -0.104 -0.183 -0.154 0.255 0.232 0.096 0.113 -0.022 0.191 0.129 1.000 -0.145 -0.071 -0.066 0.186 0.029

(15) Regulation -0.145 -0.043 -0.013 0.036 0.249 0.189 -0.191 -0.227 -0.034 -0.076 0.052 0.125 -0.131 -0.145 1.000 0.021 0.052 -0.104 -0.171

(16) CEOOwn 0.029 -0.029 -0.317 0.241 0.323 -0.222 -0.190 -0.193 -0.202 0.319 -0.209 -0.023 0.177 -0.056 0.021 1.000 0.072 -0.035 -0.015

(17) StockComp -0.040 -0.044 0.053 -0.006 0.248 0.086 -0.080 -0.212 -0.045 0.092 0.068 0.102 -0.045 -0.084 0.057 0.061 1.000 -0.421 0.101

(18) OptionComp 0.129 -0.008 0.057 -0.102 0.021 -0.194 0.090 0.121 0.028 0.195 -0.161 -0.004 0.093 0.155 -0.101 -0.035 -0.369 1.000 0.024

(19) InstOwn 0.132 0.029 -0.036 -0.092 -0.139 -0.037 0.055 0.022 -0.062 0.134 -0.015 -0.042 0.055 0.041 -0.181 -0.034 0.104 0.019 1.000

40

Table 3, continued

Panel B: Sample excluding outsider CEOs.

VARIABLE (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)

(1) MF 1.000 0.004 -0.025 -0.058 -0.066 -0.124 0.001 0.013 -0.072 0.167 -0.072 0.058 0.070 0.086 -0.152 0.056 -0.032 0.128 0.137

(2) CEOExp 0.009 1.000 0.028 0.221 0.214 -0.009 -0.093 -0.150 -0.056 0.059 -0.016 -0.026 0.060 -0.086 0.062 0.198 0.009 -0.070 -0.118

(3) EarlyTenure -0.025 0.011 1.000 -0.255 0.021 0.001 0.056 0.031 0.041 -0.005 -0.002 0.007 -0.008 0.016 -0.011 -0.262 0.016 0.058 -0.059

(4) CEOAge -0.063 0.146 -0.251 1.000 0.138 0.060 -0.079 -0.106 -0.046 -0.009 -0.013 -0.025 -0.007 -0.086 0.038 0.201 0.030 -0.104 -0.050

(5) Size -0.057 0.176 0.019 0.133 1.000 0.022 -0.237 -0.381 -0.108 0.468 -0.029 -0.085 0.006 -0.116 0.242 0.373 0.247 0.004 -0.151

(6) BTM -0.137 -0.004 -0.001 0.079 0.036 1.000 0.031 0.135 0.291 -0.252 0.285 -0.040 -0.349 -0.109 0.144 -0.213 0.030 -0.171 -0.066

(7) StdROA 0.056 -0.100 0.060 -0.110 -0.352 -0.079 1.000 0.438 0.228 -0.035 0.107 0.128 -0.098 0.170 -0.084 -0.145 -0.062 0.096 -0.007

(8) StdRet 0.038 -0.142 0.021 -0.108 -0.432 0.106 0.493 1.000 0.275 -0.091 0.112 0.044 -0.155 0.185 -0.170 -0.212 -0.186 0.175 -0.020

(9) Loss -0.072 -0.060 0.041 -0.048 -0.104 0.236 0.257 0.248 1.000 -0.128 0.246 0.045 -0.485 0.047 -0.020 -0.188 -0.031 0.036 -0.050

(10) N_Analysts 0.157 0.051 -0.004 -0.008 0.472 -0.301 -0.035 -0.063 -0.123 1.000 -0.106 0.063 0.153 0.135 -0.104 0.324 0.098 0.225 0.165

(11) ForecastErr -0.167 -0.041 -0.005 0.003 -0.078 0.355 0.204 0.188 0.282 -0.242 1.000 -0.017 -0.232 -0.022 0.016 -0.106 -0.006 -0.051 -0.047

(12) EntCost -0.034 -0.014 -0.003 0.010 0.075 0.015 0.182 -0.042 0.035 0.094 0.070 1.000 0.052 0.160 0.021 -0.058 0.092 0.016 0.068

(13) AdjROA 0.128 0.068 -0.005 -0.017 -0.065 -0.499 0.042 -0.108 -0.495 0.209 -0.263 0.064 1.000 0.105 -0.084 0.146 -0.018 0.063 0.045

(14) HighTech 0.086 -0.080 0.016 -0.092 -0.119 -0.141 0.200 0.177 0.047 0.134 -0.034 0.164 0.162 1.000 -0.132 -0.064 -0.054 0.160 0.041

(15) Regulation -0.152 0.058 -0.011 0.036 0.227 0.204 -0.193 -0.225 -0.020 -0.101 0.049 0.133 -0.154 -0.132 1.000 -0.001 0.051 -0.110 -0.201

(16) CEOOwn 0.046 0.183 -0.279 0.201 0.372 -0.227 -0.191 -0.213 -0.189 0.347 -0.201 -0.035 0.159 -0.051 0.000 1.000 0.112 -0.033 0.004

(17) StockComp -0.034 0.000 0.017 0.036 0.251 0.077 -0.073 -0.208 -0.037 0.092 0.059 0.125 -0.036 -0.066 0.054 0.102 1.000 -0.422 0.061

(18) OptionComp 0.122 -0.067 0.052 -0.090 0.027 -0.200 0.096 0.123 0.020 0.216 -0.165 -0.022 0.105 0.130 -0.108 -0.023 -0.372 1.000 0.049

(19) InstOwn 0.141 -0.126 -0.063 -0.077 -0.193 -0.048 0.090 0.088 -0.032 0.123 0.001 -0.050 0.043 0.053 -0.212 -0.021 0.055 0.043 1.000

Table 3 provides Pearson (above) and Spearman (below) correlation coefficients matrix for the sample including outsider CEOs (Panel A) and for

the sample excluding outsider CEOs (Panel B). Bold text indicates significance at the 0.10 level or better, two tailed. See Appendix A for variable

definitions.

41

Table 4

Decision to issue management forecasts and CEO internal experience

Panel A: Decision to issue management forecast(s)

MF MF

Model (1) Model (1) Model (2) Model (2)

VARIABLES Pred.

Sign

Coefficient Marginal effect VARIABLES

Pred.

Sign

Coefficient Marginal effect

(z-stat) (z-stat) (z-stat) (z-stat)

Intercept -1.6469 Intercept -1.6768

(-9.26)*** (-7.69)***

Outsider - -0.0677 -0.0129 CEOExp + 0.0119 0.0022

(-2.51)** (-2.53)** (7.09)*** (7.09)***

EarlyTenure +/- -0.1436 -0.0272 EarlyTenure +/- -0.1836 -0.0344

(-5.24)*** (-5.29)*** (-5.64)*** (-5.71)***

CEOAge - -0.0118 -0.0023 CEOAge - -0.0183 -0.0035

(-6.52)*** (-6.52)*** (-8.14)*** (-8.18)***

Size + -0.1798 -0.0344 Size + -0.2088 -0.0396

(-16.19)*** (-16.04)*** (-15.54)*** (-15.24)***

BTM +/- 0.0864 0.0165 BTM +/- 0.0454 0.0086

(2.04)** (2.04)** (0.82) (0.82)

StdROA +/- 0.2339 0.0447 StdROA +/- -0.1105 -0.0209

(0.41) (0.41) (-0.14) (-0.14)

StdRet +/- -1.9963 -0.3817 StdRet +/- -2.1338 -0.4044

(-7.14)*** (-7.12)*** (-5.91)*** (-5.88)***

Loss - -0.3408 -0.0614 Loss - -0.3573 -0.0633

(-7.49)*** (-7.92)*** (-6.08)*** (-6.46)***

N_Analysts + 0.6355 0.1215 N_Analysts + 0.6505 0.1233

(25.48)*** (24.94)*** (21.32)*** (20.63)***

ForecastErr + -18.4871 -3.5344 ForecastErr + -19.9955 -3.7895

(-4.72)*** (-4.82)*** (-3.31)*** (-3.38)***

AdjROA +/- -2.9261 -0.5594 AdjROA +/- -2.9601 -0.5610

(-5.76)*** (-5.76)*** (-4.54)*** (-4.54)***

EntCost + 0.0464 0.0089 EntCost + 0.0517 0.0098

(8.49)*** (8.49)*** (7.97)*** (7.97)***

StockComp + 0.2430 0.0465 StockComp + 0.3549 0.0673

(3.89)*** (3.89)*** (4.63)*** (4.62)***

OptionComp + 0.4247 0.0812 OptionComp + 0.4387 0.0831

(7.74)*** (7.72)*** (6.31)*** (6.29)***

CEOOwn + 0.0152 0.0029 CEOOwn + 0.0432 0.0082

(2.01)** (2.01)** (4.40)*** (4.39)***

InstOwn + 0.0105 0.0020 InstOwn + 0.0120 0.0023

(14.24)*** (14.28)*** (12.64)*** (12.69)***

HighTech + 0.2949 0.0588 HighTech + 0.1981 0.0388

(9.17)*** (8.81)*** (4.83)*** (4.68)***

Regulation - -1.3381 -0.1872 Regulation - -1.2953 -0.1822

(-18.29)*** (-28.08)*** (-15.39)*** (-23.09)***

Quarter FE Included Quarter FE Included

Year FE Included Year FE Included

MF=1 10,928 MF=1 7,538

Observations 37,625 Observations 25,948

Pseudo R2 0.0863 Pseudo R2 0.0938

Wald x2 (p-value) 3036.15 (<.001) Wald x2 (p-value) 2263.57 (<.001)

42

Table 4, continued

Panel B: Decision to issue short-horizon management forecast(s)

MF_SHRZN MF_SHRZN


VARIABLES Pred. Sign


Pred. Sign




(-12.98)*** (-11.04)***

Outsider - -0.1681 -0.0159 CEOExp + 0.0135 0.0013

(-4.73)*** (-4.84)*** (6.41)*** (6.41)***


(-5.04)*** (-5.14)*** (-5.10)*** (-5.21)***

CEOAge - -0.0070 -0.0007 CEOAge - -0.0125 -0.0012

(-3.03)*** (-3.03)*** (-4.44)*** (-4.44)***

Size + -0.2022 -0.0196 Size + -0.2291 -0.0227

(-13.54)*** (-13.37)*** (-12.85)*** (-12.58)***

BTM +/- 0.1974 0.0191 BTM +/- 0.1548 0.0153

(3.70)*** (3.74)*** (2.27)** (2.29)**

StdROA +/- 1.7179 0.1666 StdROA +/- 2.3620 0.2336

(2.46)** (2.46)** (2.52)** (2.54)**

StdRet +/- -1.8241 -0.1769 StdRet +/- -1.9664 -0.1944

(-5.09)*** (-5.08)*** (-4.24)*** (-4.24)***

Loss - -0.2005 -0.0184 Loss - -0.2746 -0.0251

(-3.41)*** (-3.56)*** (-3.64)*** (-3.89)***


(19.16)*** (18.53)*** (16.98)*** (16.36)***


(-3.61)*** (-3.74)*** (-2.42)** (-2.49)**

AdjROA +/- -3.3735 -0.3272 AdjROA +/- -3.2180 -0.3182

(-5.30)*** (-5.30)*** (-3.92)*** (-3.92)***

EntCost + 0.0344 0.0033 EntCost + 0.0374 0.0037

(5.08)*** (5.06)*** (4.88)*** (4.85)***


(4.40)*** (4.38)*** (5.06)*** (5.02)***


(2.75)*** (2.74)*** (2.52)** (2.51)**

CEOOwn + -0.0003 -0.0000 CEOOwn + 0.0122 0.0012

(-0.03) (-0.03) (0.99) (0.98)

InstOwn + 0.0120 0.0012 InstOwn + 0.0136 0.0013

(11.88)*** (11.92)*** (10.53)*** (10.60)***

HighTech + -0.1094 -0.0103 HighTech + -0.1203 -0.0115

(-2.53)** (-2.60)*** (-2.20)** (-2.27)**


(-11.47)*** (-17.90)*** (-9.32)*** (-13.91)***



MF_SHRZN =1 5,091 MF=1 3,617




43

Table 4, continued

Panel C: Decision to issue long-horizon management forecast(s)

MF_LHRZN MF_LHRZN


VARIABLES Pred.

Sign


Pred.

Sign




(-8.89)*** (-7.17)***

Outsider - 0.0509 0.0057 CEOExp + 0.0061 0.0007

(1.56) (1.55) (2.86)*** (2.86)***


(-1.53) (-1.54) (-1.91)* (-1.92)*

CEOAge - -0.0116 -0.0013 CEOAge - -0.0163 -0.0018

(-5.24)*** (-5.25)*** (-5.88)*** (-5.92)***

Size + -0.0942 -0.0105 Size + -0.1109 -0.0119

(-7.09)*** (-7.03)*** (-6.98)*** (-6.83)***

BTM +/- -0.0477 -0.0053 BTM +/- -0.0767 -0.0083

(-0.86) (-0.86) (-1.05) (-1.05)

StdROA +/- -1.1915 -0.1323 StdROA +/- -2.7008 -0.2908

(-1.65)* (-1.65)* (-2.76)*** (-2.74)***

StdRet +/- -1.4725 -0.1635 StdRet +/- -1.5355 -0.1653

(-4.34)*** (-4.31)*** (-3.51)*** (-3.46)***

Loss - -0.3461 -0.0352 Loss - -0.3084 -0.0305

(-6.04)*** (-6.56)*** (-4.18)*** (-4.48)***


(13.53)*** (13.21)*** (10.25)*** (9.82)***


(-3.20)*** (-3.28)*** (-2.38)** (-2.45)**

AdjROA +/- -1.6444 -0.1826 AdjROA +/- -1.7556 -0.1890

(-2.79)*** (-2.80)*** (-2.30)** (-2.30)**

EntCost + 0.0457 0.0051 EntCost + 0.0510 0.0055

(6.49)*** (6.50)*** (5.95)*** (5.97)***


(0.65) (0.65) (0.98) (0.98)


(6.68)*** (6.68)*** (5.27)*** (5.26)***

CEOOwn + 0.0225 0.0025 CEOOwn + 0.0549 0.0059

(2.43)** (2.43)** (4.56)*** (4.54)***

InstOwn + 0.0057 0.0006 InstOwn + 0.0064 0.0007

(6.47)*** (6.49)*** (5.68)*** (5.68)***


(13.56)*** (12.06)*** (8.17)*** (7.34)***


(-12.93)*** (-21.12)*** (-11.24)*** (-18.08)***



MF_LHRZN =1 5,837 MF=1 3,921




44

Table 4, continued

Table 4 reports logistic regression results on predicting the issuance of management forecasts based on

CEO internal experience and control variables related with managers’ forecast issuance decision. The

sample period ranges from 2001 to 2011. Panel A the dependent variable is MF which equals one for firm-

quarters in which managers issue at least one earnings forecast during the fiscal quarter, and zero otherwise.

In panels B and C, the dependent variable is MF_SHRZN and MF_LHRZN respectively, which equals one

for firm-quarters in which managers issue short-horizon and long-horizon earnings forecasts during the

fiscal quarter, respectively, and zero otherwise. A long-horizon (short-horizon) earnings forecast is defined

as a management forecast issued more than 60 days (equal to or less than 60 days) prior to the end of the

forecasting period. The coefficients’ standard errors are adjusted for firm-level clustering to account for

serial dependence across quarters of a given firm. *, **, and *** indicate significance levels at less than 10

percent, 5 percent, and 1 percent, respectively, based on two-tailed z-tests. See Appendix A for the other

variable definitions.

45

Table 5

Accuracy of management forecasts and CEO internal experience

Panel A: Accuracy of management forecasts

Accuracy Accuracy

VARIABLES Pred. Sign Model (1) VARIABLES Pred. Sign Model (2)


(-1.23) (-2.05)**

Outsider - -0.4260 CEOExp + 0.0365

(-2.71)*** (4.17)***

EarlyTenure +/- 0.2902 EarlyTenure +/- 0.2649

(2.15)** (1.79)*

CEOAge - -0.0043 CEOAge - -0.0187

(-0.43) (-1.70)*

Size + -0.0556 Size + -0.0073

(-0.71) (-0.09)

BTM +/- -3.2033 BTM +/- -2.7427

(-7.08)*** (-5.45)***

StdROA +/- -20.3556 StdROA +/- -22.2129

(-3.40)*** (-2.52)**

StdRet +/- -0.1409 StdRet +/- 0.5664

(-0.07) (0.21)

Loss - -0.7406 Loss - -1.1400

(-2.09)** (-2.41)**

Horizon - -0.0170 Horizon - -0.0197

(-4.00)*** (-4.08)***

N_Analysts + 0.3027 N_Analysts + 0.1868

(1.98)** (1.03)

ForecastErr + -378.3502 ForecastErr + -331.3336

(-5.30)*** (-3.61)***

AdjROA +/- -10.9813 AdjROA +/- -5.3719

(-2.30)** (-0.97)

EntCost + -0.0157 EntCost + -0.0834

(-0.35) (-1.70)*

StockComp + 1.2465 StockComp + 1.2110

(3.44)*** (2.83)***

OptionComp + 1.4381 OptionComp + 1.5257

(4.68)*** (4.63)***

CEOOwn + -0.1079 CEOOwn + -0.0479

(-2.33)** (-0.81)

InstOwn + 0.0323 InstOwn + 0.0293

(6.94)*** (5.86)***

HighTech + 0.4837 HighTech + 0.7100

(3.34)*** (4.02)***

Regulation - -0.5475 Regulation - -1.6533

(-0.76) (-1.92)*




Adj. R2 0.286 Adj. R2 0.267

46

Table 5, continued

Panel B: Accuracy of Short horizon and long horizon management forecasts

Accuracy Accuracy

MF_SHRZN MF_LHRZN MF_SHRZN MF_LHRZN

VARIABLES Pred.

Sign Model (1) Model (2) VARIABLES

Pred.

Sign Model (3) Model (4)

Intercept 0.0307 -0.8946 Intercept -3.1774 -2.4841

(0.02) (-0.52) (-1.73)* (-0.55)

Outsider - -0.3844 -0.5426 CEOExp + 0.0527 0.0289

(-1.68)* (-2.62)*** (4.04)*** (2.27)**

EarlyTenure +/- 0.1225 0.4037 EarlyTenure +/- 0.3303 0.0496

(0.62) (2.20)** (1.48) (0.21)

CEOAge - 0.0119 -0.0140 CEOAge - 0.0061 -0.0295

(0.91) (-0.99) (0.43) (-1.83)*

Size + -0.1838 0.0456 Size + -0.1023 -0.0630

(-1.55) (0.44) (-0.96) (-0.42)

BTM +/- -2.8900 -3.4726 BTM +/- -2.2717 -3.3997

(-5.15)*** (-5.05)*** (-4.56)*** (-4.12)***

StdROA +/- -12.4944 -24.8231 StdROA +/- -6.5134 -36.3436

(-1.94)* (-2.85)*** (-1.04) (-2.38)**

StdRet +/- -4.2389 3.0763 StdRet +/- -3.8875 3.0619

(-1.81)* (1.03) (-1.29) (0.58)

Loss - -0.4908 -1.0346 Loss - -1.5076 -0.7168

(-0.93) (-1.99)** (-2.29)** (-1.13)

Horizon - -0.0103 -0.0511 Horizon - -0.0124 -0.0637

(-1.93)* (-4.50)*** (-2.27)** (-4.22)***


(2.44)** (1.12) (1.41) (1.34)


(-2.73)*** (-5.72)*** (-2.44)** (-5.42)***

AdjROA +/- -15.6918 -7.3490 AdjROA +/- -16.7746 8.1492

(-3.61)*** (-0.92) (-2.80)*** (0.89)

EntCost + -0.0618 0.0248 EntCost + -0.0233 -0.1397

(-1.08) (0.34) (-0.34) (-1.83)*


(2.77)*** (2.97)*** (2.50)** (2.84)***


(4.35)*** (2.24)** (3.84)*** (2.29)**

CEOOwn + -0.2698 0.0148 CEOOwn + -0.1741 0.0099

(-4.18)*** (0.24) (-2.12)** (0.13)

InstOwn + 0.0297 0.0334 InstOwn + 0.0292 0.0343

(4.69)*** (5.14)*** (4.28)*** (4.01)***


(1.59) (2.47)** (2.79)*** (2.27)**

Regulation - -1.0854 0.0988 Regulation - -2.4987 -0.4832

(-0.90) (0.12) (-1.80)* (-0.50)

Quarter FE Included Included Quarter FE Included Included

Year FE Included Included Year FE Included Included

Observations 5,276 5,908 Observations 3,746 3,946

Adj. R2 0.233 0.349 Adj. R2 0.237 0.359

47

Table 5, continued

Table 5 reports OLS regression results on accuracy of management forecasts and CEO internal experience.

The sample period ranges from 2001 to 2011. The dependent variable is accuracy of management forecasts.

Panel A presents the result for whole sample of 11,184 firm-quarter observations. Panel B presents results

for accuracy of long-horizon management forecasts (MF_LHRZN) and for accuracy of short-horizon

management forecasts (MF_SHRZN). A long-horizon (short-horizon) earnings forecast is defined as a

management forecast issued more than 60 days (equal to or less than 60 days) prior to the end of the

forecasting period. For readability, all of the coefficients are multiplied by 1,000. The coefficients’ standard

errors are adjusted for firm-level clustering to account for serial dependence across quarters of a given firm.

*, **, and *** indicate significance levels at less than 10 percent, 5 percent, and 1 percent, respectively,

based on two-tailed t-tests. See Appendix A for the other variable definitions.

48

Table 6

The stock price response to management forecasts and CEO internal experience

Panel A: Descriptive Statistics

Variable N Mean Std Dev P10 P25 Median P75 P90

CAR(-1, +1) 10,726 0.001 0.086 -0.094 -0.038 0.002 0.045 0.096

News 10,726 -0.003 0.056 -0.005 -0.002 0.000 0.001 0.006

OUTSIDER 10,726 0.315 0.464 0.000 0.000 0.000 1.000 1.000

CEOExp 7,350 11.493 9.092 2.000 4.000 9.000 17.000 25.000

EarlyTenure 10,726 0.350 0.477 0.000 0.000 0.000 1.000 1.000

CEOAge 10,726 54.711 6.951 46.000 50.000 55.000 59.000 64.000

Size 10,726 7.554 1.471 5.777 6.485 7.456 8.492 9.561

BTM 10,726 0.458 0.313 0.173 0.265 0.392 0.572 0.800

StdROA 10,726 0.017 0.026 0.003 0.005 0.009 0.018 0.036

Horizon 10,726 54.720 23.560 19.000 46.000 62.000 69.000 73.000

MFloss 10,726 0.051 0.221 0.000 0.000 0.000 0.000 0.000

Point 10,726 0.125 0.331 0.000 0.000 0.000 0.000 1.000

49

Table 6, continued

Panel B: Regression results

CAR(-1, +1) CAR(-1, +1)

VARIABLES Pred. Sign Model (1) VARIABLES Pred. Sign Model (2)

Intercept 0.0078 Intercept 0.0028

(0.24) (0.23)

Outsider ? 0.0012 CEOExp ? 0.0000

(0.63) (0.40)

Outsider*News - -0.3841 CEOExp*News + 0.0890

(-4.45)*** (5.60)***

News + 5.7403 News + 9.9606

(7.21)*** (8.76)***

EarlyTenure*News +/- 0.5032 EarlyTenure*News +/- 0.4142

(2.67)*** (1.34)

CEOAge*News +/- -0.0376 CEOAge*News +/- -0.0700

(-3.93)*** (-4.42)***

Size*News +/- -0.3331 Size*News +/- -0.6599

(-5.66)*** (-7.28)***

BTM*News - -0.0210 BTM*News - -0.0287

(-0.23) (-0.25)

StdROA*News +/- 1.0144 StdROA*News +/- -2.9242

(0.46) (-1.23)

Horizon*News - -0.0013 Horizon*News - -0.0004

(-1.29) (-0.45)

MFloss*News - -1.0615 MFloss*News - -1.3700

(-4.12)*** (-5.29)***

Point*News + 0.1267 Point*News + 0.6827

(1.94)* (1.46)

EarlyTenure +/- -0.0014 EarlyTenure +/- -0.0016

(-0.76) (-0.80)

CEOAge +/- 0.0001 CEOAge +/- 0.0000

(0.53) (0.22)

Size +/- -0.0015 Size +/- -0.0016

(-2.66)*** (-2.44)**

BTM +/- 0.0077 BTM +/- 0.0155

(2.20)** (3.81)***

StdROA +/- -0.0018 StdROA +/- -0.0535

(-0.04) (-0.82)

Horizon +/- 0.0003 Horizon +/- 0.0002

(6.41)*** (4.84)***

MFloss - -0.0333 MFloss - -0.0216

(-6.04)*** (-3.07)***

Point + 0.0016 Point + 0.0039

(0.66) (1.42)




Adj. R2 0.055 Adj. R2 0.083

50

Table 6, continued

Panel A presents descriptive statistics for the 10,726 firm-year observations with available data on the

market’s reaction to management forecasts, management forecast news, and control variables that affect

the market’s reaction to management forecasts. The sample period ranges from 2001 to 2011. Panel B

reports OLS regression results of the stock price response to management forecasts on CEO internal

experience. The dependent variable is CAR(-1, +1), defined as three-day cumulative market adjusted stock

returns around the management earnings forecast issuance date. The coefficients’ standard errors are

adjusted for firm-level clustering to account for serial dependence across years of a given firm. *, **, and

*** indicate significance levels at less than 10 percent, 5 percent, and 1 percent, respectively, based on

two-tailed t-tests. See Appendix A for variable definitions.

51

Table 7

Management forecasts and CEO internal experience: Controlling for pre-turnover performance

Panel A: Insider CEOs vs. outsider CEOs

MF Accuracy CAR(-1, +1)

VARIABLES Model (1) VARIABLES Model (2) VARIABLES Model (3)

Intercept -1.2815 Intercept 1.4975 Intercept 0.0077

(-6.28)*** (1.29) (0.67)

Outsider 0.0409 Outsider -0.4278 Outsider -0.0001

(1.23) (-2.10)** (-0.06)

EarlyTenure -0.2128 EarlyTenure 0.1514 Outsider*News -0.8564

(-7.11)*** (1.07) (-3.07)***

CEOAge -0.0225 CEOAge -0.0236 News 6.6741

(-10.03)*** (-2.04)** (4.73)***

Size -0.2062 Size -0.1433 EarlyTenure*News 0.0366

(-15.92)*** (-1.69)* (0.12)

BTM 0.1406 BTM -2.7537 CEOAge*News -0.0264

(2.91)*** (-6.22)*** (-1.55)

StdROA 0.2871 StdROA -10.3669 Size*News -0.4499

(0.39) (-1.63) (-5.11)***

StdRet -2.3886 StdRet -2.8791 BTM*News 0.0529

(-7.04)*** (-1.13) (0.42)

Loss -0.3356 Loss -0.6145 StdROA*News -0.8121

(-6.42)*** (-1.39) (-0.58)

N_Analysts 0.6462 Horizon -0.0179 Horizon*News -0.0018

(22.15)*** (-3.87)*** (-1.02)

ForecastErr -19.9584 N_Analysts 0.2867 MFloss*News -1.2544

(-4.56)*** (1.54) (-4.62)***

AdjROA -2.1149 ForecastErr -471.6409 Point*News 0.1603

(-3.47)*** (-6.11)*** (2.24)**

EntCost 0.0570 AdjROA -11.5614 PreturnoverROA*News 2.5704

(8.73)*** (-2.08)** (2.43)**

StockComp 0.2365 EntCost -0.0263 EarlyTenure -0.0012

(3.24)*** (-0.51) (-0.61)

OptionComp 0.3558 StockComp 0.8292 CEOAge -0.0001

(5.30)*** (2.31)** (-0.43)

CEOOwn 0.0595 OptionComp 1.0628 Size -0.0015

(6.05)*** (2.96)*** (-2.25)**

InstOwn 0.0104 CEOOwn -0.1152 BTM 0.0120

(12.20)*** (-2.15)** (2.84)***

HighTech 0.3449 InstOwn 0.0250 StdROA 0.0291

(8.79)*** (5.21)*** (0.51)

Regulation -1.3430 HighTech 0.3220 Horizon 0.0002

(-16.70)*** (1.66)* (4.84)***

PreturnoverROA -0.5712 Regulation -0.3583 MFloss -0.0329

(-3.75)*** (-0.50) (-5.21)***

PreturnoverROA 1.7727 Point 0.0014

(1.43) (0.48)

PreturnoverROA -0.0155

(-1.48)

Quarter FE Included Quarter FE Included Quarter FE Included

Year FE Included Year FE Included Year FE Included

Observations 28,797 Observations 8,509 Observations 7,738

Pseudo R2 0.094 Adj. R2 0.318 Adj. R2 0.060

52

Table 7, continued

Panel B: Variations in CEO internal experience



Intercept -1.2955 Intercept 0.9516 Intercept 0.0034

(-5.40)*** (0.85) (0.26)

CEOExp 0.0123 CEOExp 0.0215 CEOExp 0.0001

(6.79)*** (2.37)** (0.80)

EarlyTenure -0.2238 EarlyTenure 0.1168 CEOExp*News 0.0523

(-6.43)*** (0.73) (2.73)***


(-10.24)*** (-2.15)** (7.08)***

Size -0.2179 Size -0.0396 EarlyTenure*News -0.2834

(-14.74)*** (-0.43) (-0.71)


(1.12) (-4.18)*** (-2.35)**

StdROA -0.6892 StdROA -13.4459 Size*News -0.6840

(-0.72) (-1.48) (-6.56)***

StdRet -2.7057 StdRet -1.0397 BTM*News -0.1647

(-6.57)*** (-0.33) (-0.51)


(-5.23)*** (-0.43) (-0.52)


(19.36)*** (-3.95)*** (-2.52)**

ForecastErr -24.7345 N_Analysts -0.0010 MFloss*News -1.5418

(-3.87)*** (-0.00) (-5.90)***


(-3.64)*** (-9.67)*** (1.62)

EntCost 0.0547 AdjROA -0.2145 PreturnoverROA*News 2.9314

(7.49)*** (-0.04) (1.41)


(5.84)*** (-0.54) (-1.33)


(5.73)*** (1.13) (-0.58)


(5.36)*** (2.91)*** (-1.61)


(11.46)*** (-1.02) (3.88)***


(6.42)*** (3.96)*** (1.01)


(-14.81)*** (4.18)*** (4.18)***

PreturnoverROA -0.9902 Regulation -1.1259 MFloss -0.0238

(-5.85)*** (-1.36) (-3.08)***

PreturnoverROA -0.2018 Point 0.0044

(-0.26) (1.51)

PreturnoverROA -0.0127

(-0.96)





53

Table 7, continued

Table 7 column (1) reports logistic regression results on predicting the issuance of management forecasts

based on CEO internal experience after controlling for CEO pre-turnover firm performance. Column (2)

reports OLS regression results on accuracy of management forecasts and CEO internal experience and

column (3) reports the stock price response to management forecasts on CEO internal experience after

controlling for CEO pre-turnover firm performance. CEO pre-turnover firm performance is measured by

average industry adjusted return on assets over past two consecutive years before CEO turnover. The

coefficients’ standard errors are adjusted for firm-level clustering to account for serial dependence across

years of a given firm. *, **, and *** indicate significance levels at less than 10 percent, 5 percent, and 1

percent, respectively, based on two-tailed z-tests and t-tests. See Appendix A for variable definitions.

54

Table 8

Management forecasts and CEO internal experience: Controlling for CEO ability

Panel A: Insider vs. outsider CEOs



Intercept -0.2266 Intercept -4.0693 Intercept 0.0161

(-1.09) (-3.00)*** (0.50)

Outsider -0.0629 Outsider -0.2473 Outsider 0.0020

(-2.13)** (-1.67)* (1.01)

EarlyTenure -0.1825 EarlyTenure -0.0778 Outsider*News -0.7927

(-5.95)*** (-0.58) (-4.38)***


(-7.12)*** (-1.14) (6.39)***

Size -0.1218 Size 0.1239 EarlyTenure*News 0.3735

(-9.36)*** (1.72)* (1.84)*


(2.63)*** (-6.97)*** (-3.66)***


(-0.33) (-3.61)*** (-4.34)***

StdRet -2.2789 StdRet 1.8939 BTM*News -0.0067

(-7.27)*** (0.93) (-0.07)


(-9.63)*** (-3.22)*** (-1.93)*


(19.67)*** (-2.39)** (-0.20)


(-4.38)*** (1.67)* (-6.66)***


(-5.23)*** (-4.71)*** (2.00)**

EntCost -0.1029 AdjROA -9.2379 MAScore*News -1.0669

(-10.72)*** (-1.72)* (-0.91)


(3.67)*** (-0.14) (-1.18)

OptionComp 0.3905 StockComp 1.2487 CEOAge 0.0000

(6.43)*** (3.49)*** (0.11)


(2.40)** (4.56)*** (-2.10)**


(11.50)*** (-1.93)* (2.76)***


(10.45)*** (6.70)*** (0.79)


(-7.36)*** (1.15) (5.31)***

MAScore -0.5843 Regulation -4.7288 MFloss -0.0353

(-5.93)*** (-2.42)** (-6.05)***

MAScore 0.1303 Point 0.0016

(0.25) (0.58)

MAScore 0.0085

(1.21)





55

Table 8, continued

Panel B: Variations in CEO internal experience



Intercept -0.2874 Intercept -5.5875 Intercept 0.0126

(-1.17) (-2.65)*** (0.32)

CEOExp 0.0064 CEOExp 0.0408 CEOExp -0.0000

(3.55)*** (4.74)*** (-0.29)

EarlyTenure -0.1828 EarlyTenure 0.1643 CEOExp*News 0.1182

(-5.25)*** (1.13) (6.33)***


(-8.45)*** (-2.55)** (4.58)***

Size -0.1310 Size 0.0780 EarlyTenure*News 0.7382

(-8.39)*** (1.05) (1.86)*


(2.33)** (-5.36)*** (-1.94)*


(-0.89) (-2.78)*** (-5.45)***

StdRet -2.3252 StdRet 3.1573 BTM*News 0.0678

(-6.25)*** (1.18) (0.26)


(-6.74)*** (-2.20)** (-0.45)


(17.39)*** (-4.54)*** (-2.28)**


(-3.34)*** (0.88) (-4.07)***


(-4.16)*** (-3.18)*** (1.09)

EntCost -0.1075 AdjROA -4.2977 MAScore*News 0.7512

(-9.29)*** (-0.77) (0.51)


(4.30)*** (-0.06) (-1.05)


(5.14)*** (3.26)*** (-0.06)


(3.83)*** (5.31)*** (-1.34)


(10.45)*** (-0.39) (3.98)***


(5.61)*** (6.18)*** (0.14)


(-7.58)*** (2.92)*** (4.26)***

MAScore -0.4397 Regulation -8.2186 MFloss -0.0229

(-3.78)*** (-3.69)*** (-3.06)***

MAScore -0.5426 Point 0.0041

(-0.91) (1.36)

MAScore 0.0063

(0.77)





56

Table 8, continued

Table 8 column (1) reports logistic regression results on predicting the issuance of management forecasts

based on CEO internal experience after controlling for CEO ability proxies. Column (2) reports OLS

regression results on accuracy of management forecasts and CEO internal experience and column (3)

reports the stock price response to management forecasts on CEO internal experience after controlling for

CEO ability proxies. CEO ability proxy is MAScore which is managerial ability measure by Demerjian et

al. (2012). The coefficients’ standard errors are adjusted for firm-level clustering to account for serial

dependence across years of a given firm. *, **, and *** indicate significance levels at less than 10 percent,

5 percent, and 1 percent, respectively, based on two-tailed z-tests and t-tests. See Appendix A for variable

definitions.

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